Dissertations / Theses: 'Model-based predictive control (MBPC)'

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Muslim, Abrar. "Optimisation of chlorine dosing for water disribution system using model-based predictive control." Thesis, Curtin University, 2007. http://hdl.handle.net/20.500.11937/459.

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An ideal drinking water distribution system (DWDS) must supply safe drinking water with free chlorine residual (FCR) in the form of HOCI and OCIֿ at a required concentration level. Meanwhile the FCR is consumed in the bulk liquid phase and at the DWDS pipes wall as the result of chemical reactions. Because of these, an optimized chlorine dosing for the DWDS using model-based predictive control (MBPC) is developed through the steps of modelling the FCR transport along the main pipes of the DWDS, designing chlorine dosing and implementing a multiple-input multiple-output system control scheme in Matlab 7.0.1 software. Discrete time-space models (DTSM) that can be used to predict free chlorine residual (FCR) concentration along the pipes of the DWDS over time is developed using explicit finite difference method (EFDM). Simulations of the DTSM using step and rectangular pulse input show that the effect of water flow rate velocity is much stronger than the effect of chlorine effective diffusivity coefficient on the FCR distribution and decay process in the DWDS main pipes. Therefore, the FCR axial diffusion in single pipes of the DWDS can be neglected. Investigating the effect of injection time, initial chlorine distribution, and overall chlorine decay rate constant involved in the process have provided a thorough understanding of chlorination and the effectiveness of all the parameters. This study proposed a model-based chlorine dosing design (MBCDD) based on a conventional-optimum design process (CODP) (Aurora, 2004), which is created for uncertain water demand based on the DTSM simulation.In the MBCDD, the constraints must be met by designing distances between chlorine boosters and optimal value of the initial chlorine distribution in order to maintain the controlled variable (CV), i.e. FCR concentration with a certain degree of robustness to the variations of water flow rate. The MBCDD can cope with the simulated DWDS (SDWDS) with the conditions; the main pipe is 12 inch diameter size with the pipe length of 8.5 km, the first consumers taking the water from the point of 0.83 km, the assumed pipe wall chlorine decay rate constant of 0.45 m/day, and the value of chlorine overall decay rate constants follow Rosman's model (1994), by proposing a set of rules for selecting the locations for additional chlorine dosing boosters, and setting the optimal chlorine dosing concentrations for each booster in order to maintain a relatively even FCR distribution along the DWDS, which is robust against volumetric water supply velocity (VWS) variations. An example shows that by implementing this strategy, MBCDD can control the FCR along the 8.5 km main pipe of 12 inch diameter size with the VWS velocity from 0.2457 to 2.457 km/hr and with the assumed wall and bulk decay constants of 0.45 and 0.55 m/day, respectively. An adaptive chlorine dosing design (ACDD) as another CODP of chlorine dosing which has the same concept with the MBCDD without the rule of critical velocity is also proposed in this study. The ACDD objective is to obtain the optimum value of initial chlorine distribution for every single change in the VWS. Simulation of the ACDD on the SDWDS shows that the ACDD can maintain the FCR concentration within the required limit of 0.2-0.6 mg/1.To enable water quality modelling for studying the effectiveness of chlorine dosing and injection in the form of mass flow rate of pure gaseous chlorine as manipulated variable (MV), a multiple-input multiple-output (MIMO) system is developed in Simulink for Matlab 7.0.1 software by considering the disturbances of temperature and circuiting flow. The MIMO system can be used to design booster locations and distribution along a main pipe of the DWDS, to monitor the FCR concentration at the point just before injection (mixing) and between two boosters, and to implement feedback and open-loop control. This study also proposed a decentralized model-based control (DMBC) based on the MBCDD-ACDD and centralized model predictive control (CMPC) in order to optimize MV to control the CV along the main pipe of the DWDS in the MIMO system from the FCR concentration at just after the chlorine injection (CVin) to the FCR concentration (CVo) before the next chlorine injection with the constraints of 0.2-0.6 ppm for both the CVin and CVo. A comparison of the performances of decentralized PI (DPI) control, DMBC and CMPC, shows that the performances of the DMBC and CMPC in controlling the MIMO system are almost the same, and they both are significantly better than the DPI control performance. In brief, model-based predictive control (MBPC), in this case a decentralized model-based control (DMBC) and a centralized predictive control (CMPC), enable optimization of chlorine dosing for the DWDS.

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Muslim, Abrar. "Optimisation of chlorine dosing for water disribution system using model-based predictive control." Curtin University of Technology, Dept. of Chemical Engineering, 2007. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=21508.

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An ideal drinking water distribution system (DWDS) must supply safe drinking water with free chlorine residual (FCR) in the form of HOCI and OCIֿ at a required concentration level. Meanwhile the FCR is consumed in the bulk liquid phase and at the DWDS pipes wall as the result of chemical reactions. Because of these, an optimized chlorine dosing for the DWDS using model-based predictive control (MBPC) is developed through the steps of modelling the FCR transport along the main pipes of the DWDS, designing chlorine dosing and implementing a multiple-input multiple-output system control scheme in Matlab 7.0.1 software. Discrete time-space models (DTSM) that can be used to predict free chlorine residual (FCR) concentration along the pipes of the DWDS over time is developed using explicit finite difference method (EFDM). Simulations of the DTSM using step and rectangular pulse input show that the effect of water flow rate velocity is much stronger than the effect of chlorine effective diffusivity coefficient on the FCR distribution and decay process in the DWDS main pipes. Therefore, the FCR axial diffusion in single pipes of the DWDS can be neglected. Investigating the effect of injection time, initial chlorine distribution, and overall chlorine decay rate constant involved in the process have provided a thorough understanding of chlorination and the effectiveness of all the parameters. This study proposed a model-based chlorine dosing design (MBCDD) based on a conventional-optimum design process (CODP) (Aurora, 2004), which is created for uncertain water demand based on the DTSM simulation.
In the MBCDD, the constraints must be met by designing distances between chlorine boosters and optimal value of the initial chlorine distribution in order to maintain the controlled variable (CV), i.e. FCR concentration with a certain degree of robustness to the variations of water flow rate. The MBCDD can cope with the simulated DWDS (SDWDS) with the conditions; the main pipe is 12 inch diameter size with the pipe length of 8.5 km, the first consumers taking the water from the point of 0.83 km, the assumed pipe wall chlorine decay rate constant of 0.45 m/day, and the value of chlorine overall decay rate constants follow Rosman's model (1994), by proposing a set of rules for selecting the locations for additional chlorine dosing boosters, and setting the optimal chlorine dosing concentrations for each booster in order to maintain a relatively even FCR distribution along the DWDS, which is robust against volumetric water supply velocity (VWS) variations. An example shows that by implementing this strategy, MBCDD can control the FCR along the 8.5 km main pipe of 12 inch diameter size with the VWS velocity from 0.2457 to 2.457 km/hr and with the assumed wall and bulk decay constants of 0.45 and 0.55 m/day, respectively. An adaptive chlorine dosing design (ACDD) as another CODP of chlorine dosing which has the same concept with the MBCDD without the rule of critical velocity is also proposed in this study. The ACDD objective is to obtain the optimum value of initial chlorine distribution for every single change in the VWS. Simulation of the ACDD on the SDWDS shows that the ACDD can maintain the FCR concentration within the required limit of 0.2-0.6 mg/1.
To enable water quality modelling for studying the effectiveness of chlorine dosing and injection in the form of mass flow rate of pure gaseous chlorine as manipulated variable (MV), a multiple-input multiple-output (MIMO) system is developed in Simulink for Matlab 7.0.1 software by considering the disturbances of temperature and circuiting flow. The MIMO system can be used to design booster locations and distribution along a main pipe of the DWDS, to monitor the FCR concentration at the point just before injection (mixing) and between two boosters, and to implement feedback and open-loop control. This study also proposed a decentralized model-based control (DMBC) based on the MBCDD-ACDD and centralized model predictive control (CMPC) in order to optimize MV to control the CV along the main pipe of the DWDS in the MIMO system from the FCR concentration at just after the chlorine injection (CVin) to the FCR concentration (CVo) before the next chlorine injection with the constraints of 0.2-0.6 ppm for both the CVin and CVo. A comparison of the performances of decentralized PI (DPI) control, DMBC and CMPC, shows that the performances of the DMBC and CMPC in controlling the MIMO system are almost the same, and they both are significantly better than the DPI control performance. In brief, model-based predictive control (MBPC), in this case a decentralized model-based control (DMBC) and a centralized predictive control (CMPC), enable optimization of chlorine dosing for the DWDS.

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Santana, Eudemario Souza de. "Algoritmo preditivo baseado em modelo aplicado ao controle de velocidade do motor de indução." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260709.

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Orientador: Edson Bim
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
Made available in DSpace on 2018-08-09T18:47:38Z (GMT). No. of bitstreams: 1 Santana_EudemarioSouzade_D.pdf: 2512813 bytes, checksum: 189069455159efa5f8327460e869f749 (MD5) Previous issue date: 2007
Resumo: Esta tese trata do emprego do controle preditivo baseado no modelo (MBPC-Model Based Predictive Control) no acionamento do motor de indução do trifásico, para controle de fluxo de rotor e velocidade. A estratégia MBPC baseia-se na minimização do erro entre as referências futuras e a predição do modelo, para gerar os sinais de controle. Nesta tese, o motor de indução é descrito por espaço de estados e, diferentemente, do MBPC não linear, que emprega algoritmos de busca para determinar os sinais de controle, a estratégia escolhida faz inearizações sucessivas. Assim sendo, a cada ciclo gera-se a lei de controle, sendo que esta é dada por uma equação algébrica. São necessários ao controlador preditivo o conhecimento das tensões de terminal do estator e das seguintes variáveis de estado: corrente de estator, fluxo de rotor e velocidade de eixo. Para a estimação dos estados é empregado o filtro de Kalman estendido. O torque de carga é tratato como uma perturbação e sua magnitude é obtida por duas abordagens: pela equação eletromecânica e pelo filtro de Kalman estendido. Resultados de simulação computacional e experimentais validam a proposta
Abstract: This thesis presents the results concerning the control of rotor flux and speed of the induction motor using MBPC strategy, which is based on the error minimization between the future set point and model prediction, resulting in control signals. In the case studied in this thesis the motor model is described in space-state. The non linear MBPC emploies search algorithms to find the control signals, whereas the technique used in this thesis made sucessives linearizations on model; therefore in every control cicle a new algebraic control lay is found. The predictive control needs to know the stator voltage and the following state variables: stator current, rotor flux and speed. In the order to estimate the states an extended Kalman filter is employed. The load torque is considered as a disturbance and its amplitude is obtained in two ways: by calculation via eletromechanical equation and by estimation via Kalman filter. The proposal has been validated by imulations and experiments
Doutorado
Energia Eletrica
Doutor em Engenharia Elétrica

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Kandiah, Sivasothy. "Fuzzy model based predictive control of chemical processes." Thesis, University of Sheffield, 1996. http://etheses.whiterose.ac.uk/3029/.

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The past few years have witnessed a rapid growth in the use of fuzzy logic controllers for the control of processes which are complex and ill-defined. These control systems, inspired by the approximate reasoning capabilities of humans under conditions of uncertainty and imprecision, consist of linguistic 'if-then' rules which depend on fuzzy set theory for representation and evaluation using computers. Even though the fuzzy rules can be built from purely heuristic knowledge such as a human operator's control strategy, a number of difficulties face the designer of such systems. For any reasonably complex chemical process, the number of rules required to ensure adequate control in all operating regions may be extremely large. Eliciting all of these rules and ensuring their consistency and completeness can be a daunting task. An alternative to modelling the operator's response is to model the process and then to incorporate the process model into some sort of model-based control scheme. The concept of Model Based Predictive Control (MB PC) has been heralded as one of the most significant control developments in recent years. It is now widely used in the chemical and petrochemical industry and it continues to attract a considerable amount of research. Its popularity can be attributed to its many remarkable features and its open methodology. The wide range of choice of model structures, prediction horizon and optimisation criteria allows the control designer to easily tailor MBPC to his application. Features sought from such controllers include better performance, ease of tuning, greater robustness, ability to handle process constraints, dead time compensation and the ability to control nonminimum phase and open loop unstable processes. The concept of MBPC is not restricted to single-input single-output (SISO) processes. Feedforward action can be introduced easily for compensation of measurable disturbances and the use of state-space model formulation allows the approach to be generalised easily to multi-input multi-output (MIMO) systems. Although many different MBPC schemes have emerged, linear process models derived from input-output data are often used either explicitly to predict future process behaviour and/or implicitly to calculate the control action even though many chemical processes exhibit nonlinear process behaviour. It is well-recognised that the inherent nonlinearity of many chemical processes presents a challenging control problem, especially where quality and/or economic performance are important demands. In this thesis, MBPC is incorporated into a nonlinear fuzzy modelling framework. Even though a control algorithm based on a 1-step ahead predictive control strategy has initially been examined, subsequent studies focus on determining the optimal controller output using a long-range predictive control strategy. The fuzzy modelling method proposed by Takagi and Sugeno has been used throughout the thesis. This modelling method uses fuzzy inference to combine the outputs of a number of auto-regressive linear sub-models to construct an overall nonlinear process model. The method provides a more compact model (hence requiring less computations) than fuzzy modelling methods using relational arrays. It also provides an improvement in modelling accuracy and effectively overcomes the problems arising from incomplete models that characterise relational fuzzy models. Difficulties in using traditional cost function and optimisation techniques with fuzzy models have led other researchers to use numerical search techniques for determining the controller output. The emphasis in this thesis has been on computationally efficient analytically derived control algorithms. The performance of the proposed control system is examined using simulations of the liquid level in a tank, a continuous stirred tank reactor (CSTR) system, a binary distillation column and a forced circulation evaporator system. The results demonstrate the ability of the proposed system to outperform more traditional control systems. The results also show that inspite of the greatly reduced computational requirement of our proposed controller, it is possible to equal or better the performance of some of the other fuzzy model based control systems that have been proposed in the literature. It is also shown in this thesis that the proposed control algorithm can be easily extended to address the requirements of time-varying processes and processes requiring compensation for disturbance inputs and dead times. The application of the control system to multivariable processes and the ability to incorporate explicit constraints in the optimisation process are also demonstrated.

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Choi, Il Seop. "Model-based predictive control for hot rolling mills." Thesis, University of Sheffield, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434493.

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Paulus, Amanda. "A Model-Predictive-Control Based Smart-Grid Aggregator." Thesis, KTH, Optimeringslära och systemteori, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230958.

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Intermittent energy source usage, such as solar and wind power, is continuously increasing. Intermittent energy sources are highly dependent on prevailing weather conditions, resulting in stochastic electricity generation. The expected stochasticity in electricity generation will cause issues for the current power grid. Moreover, an expected issue for the Swedish power grid is higher peak loads. Thus, there is an emerging need for novel and smart power systems capable of shifting peak loads in the future electricity grid. Model Predictive Control (MPC) is a sophisticated control method that is suitable for smart-grid aggregators. Hence, MPC can be used to optimally control the efficiency of energy use in a smart grid and shift peak loads. The purpose of this thesis is to investigate optimal peak load-shifting and efficiency of electrical substation operation in a smart grid in Ramsjöåsen, Sweden, using an MPC based smart-grid aggregator. Furthermore, the purpose is also to contribute to the theoretical foundation for future peak load-shifting in smart grids. Within the thesis project a mathematical model for the smart grid in Ramsjöåsen is developed, which is then used to simulate different scenarios. The simulated results indicate that an MPC based smart-grid aggregator improves the performance of the smart grid in Ramsjöåsen, as regards to both peak load-shifting and efficiency of electrical substation operation.
Användningen av intermittenta energikällor, såsom sol och vindkraft, ökar ständigt. Intermittenta energikällor är starkt beroende av rådande väderförhållanden, vilket resulterar i stokastisk elproduktion. Den förväntade stokasticiteten i elproduktion kommer att orsaka problem för det nuvarande elnätet. Dessutom förväntas högre toppbelastningar för det svenska elnätet. Således finns ett växande behov av nya och smarta kraftsystem som kan reducera toppbelastningar i det framtida elnätet. Model Predictive Control (MPC) är en sofistikerad styrningsmetod som är lämplig för smart-näts aggregatorer. Därav kan MPC användas för att optimalt styra effektivitet av energianvändning i ett smart nät och minska toppbelastningar. Syftet med detta examensarbete är att undersöka optimal reducering av toppbelastningar och drift-effektivitet av transformatorstationen i ett smart nät i Ramsjöåsen, Sverige, med hjälp av en MPC baserad smart-näts aggregator. Dessutom är syftet att bidra till den teoretiska grunden för framtida topplastskapning i smarta nät. Inom examensarbetsprojektet utvecklas en matematisk modell för smart nätet i Ramsjöåsen, som sedan används för att simulera olika scenarier. De simulerade resultaten indikerar att en MPC baserad smart-näts aggregator förbättrar smart nätets prestanda i Ramsjöåsen, vad gäller både topplastsreducering och drifteffektivitet av transformatorstationen.

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Hozumi, Yuya, Shinji Doki, and Shigeru Okuma. "Fast torque control system of PMSM based on model predictive control." IEEE, 2009. http://hdl.handle.net/2237/13963.

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MacKay, Maria Ellen. "Model based predictive control of nonlinear and multivariable systems." Thesis, Manchester Metropolitan University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337269.

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Droge, Greg Nathanael. "Behavior-based model predictive control for networked multi-agent systems." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51864.

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We present a motion control framework which allows a group of robots to work together to decide upon their motions by minimizing a collective cost without any central computing component or any one agent performing a large portion of the computation. When developing distributed control algorithms, care must be taken to respect the limited computational capacity of each agent as well as respect the information and communication constraints of the network. To address these issues, we develop a distributed, behavior-based model predictive control (MPC) framework which alleviates the computational difficulties present in many distributed MPC frameworks, while respecting the communication and information constraints of the network. In developing the multi-agent control framework, we make three contributions. First, we develop a distributed optimization technique which respects the dynamic communication restraints of the network, converges to a collective minimum of the cost, and has transients suitable for robot motion control. Second, we develop a behavior-based MPC framework to control the motion of a single-agent and apply the framework to robot navigation. The third contribution is to combine the concepts of distributed optimization and behavior-based MPC to develop the mentioned multi-agent behavior-based MPC algorithm suitable for multi-robot motion control.

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Huzmezan, Mihai. "Theory and aerospace applications of constrained model based predictive control." Thesis, University of Cambridge, 1998. https://www.repository.cam.ac.uk/handle/1810/272419.

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Buqueras, Carbonell Carles. "Model-based predictive control using Modelica and open source components." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9120.

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This thesis is about Model Predictive Control (MPC) method for process control. It describes how this method could be implemented using some different open source software components, describing functionalities of each one and showing how the implementation has been done. Finally the code is tested to demonstrate effectiveness of this software in front of this kind of problems and to demonstrate MPC main characteristics. The main goals of this thesis are these last ones, code development and tests, so all mathematical and theoretical background are described but not as in detail as development and tests. Globally describing, MPC is a process control method where a previous knowledge of the plant is needed, so the controller have a model to simulate and predict the behavior of the system to calculate the best command signal. It has an optimization algorithm determining the optimal trajectory to bring system from initial state to desired state. Optimization is done by iterative simulation and solved online periodically at each sample time, initializing values at each time with measured feedback.

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Groß, Dominic [Verfasser]. "Distributed Model Predictive Control with Event-Based Communication / Dominic Groß." Kassel : Kassel University Press, 2015. http://d-nb.info/107453123X/34.

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Robb, David MacKenzie. "Model based predictive control with application to renewable energy systems." Thesis, University of Strathclyde, 2000. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=20379.

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In the promotion and development of renewable energy systems, control engineering is one area which can directly affect the overall system performance and economics and thus help to make renewable energies more attractive and popular. For cost effectiveness, ideally the renewable energy industry requires a control design technique which is very effective yet simple with methods that are transparent enough to allow implementation by non-control engineers. The objective of this thesis is to determine if Model Based Predictive Control (MBPC) is a suitable control technique for use by the renewable energy industry. MBPC is chosen as it uses simple and fairly transparent methods yet claims to be powerful and can deal with issues, such as non linearities and controller constraints, which are important in renewable energy systems. MBPC is applied to a solar power parabolic trough system and a variable speed wind turbine to enable the general applicability of MBPC to renewable energy systems to be tested and the possible benefits to the industry to be assessed. Also by applying the MBPC technique to these two strongly contrasting systems much experience is gained about the MBPC technique itself, and its strengths and weaknesses and ease of application are assessed. The investigation into the performance of Model Based Predictive Control and in particular its application in the renewable energy industry leads to two contrasting conclusions. For simple systems with non-demanding dynamics and having a good model of the system, MBPC provides a very good and effective solution. However for more demanding systems with complex dynamics and strong non-linearities, a basic MBPC controller, applied by a non-control engineer, cannot be recommended.

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Abdelghafar, Osman Haitham Mohamed Osman. "Tuning model based predictive control using multi-objective evolution algorithms." Thesis, University of Newcastle Upon Tyne, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.420026.

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Yang, Xiaoke. "Fault-tolerant predictive control : a Gaussian process model based approach." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708784.

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Munoz, Carpintero Diego Alejandro. "Strategies in robust and stochastic model predictive control." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:2f6bce71-f91f-4d5a-998f-295eff5b089a.

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The presence of uncertainty in model predictive control (MPC) has been accounted for using two types of approaches: robust MPC (RMPC) and stochastic MPC (SMPC). Ideal RMPC and SMPC formulations consider closed-loop optimal control problems whose exact solution, via dynamic programming, is intractable for most systems. Much effort then has been devoted to find good compromises between the degree of optimality and computational tractability. This thesis expands on this effort and presents robust and stochastic MPC strategies with reduced online computational requirements where the conservativeness incurred is made as small as conveniently possible. Two RMPC strategies are proposed for linear systems under additive uncertainty. They are based on a recently proposed approach which uses a triangular prediction structure and a non-linear control policy. One strategy considers a transference of part of the computation of the control policy to an offline stage. The other strategy considers a modification of the prediction structure so that it has a striped structure and the disturbance compensation extends throughout an infinite horizon. An RMPC strategy for linear systems with additive and multiplicative uncertainty is also presented. It considers polytopic dynamics that are designed so as to maximize the volume of an invariant ellipsoid, and are used in a dual-mode prediction scheme where constraint satisfaction is ensured by an approach based on a variation of Farkas' Lemma. Finally, two SMPC strategies for linear systems with additive uncertainty are presented, which use an affine-in-the-disturbances control policy with a striped structure. One strategy considers an offline sequential design of the gains of the control policy, while these are variables in the online optimization in the other. Control theoretic properties, such as recursive feasibility and stability, are studied for all the proposed strategies. Numerical comparisons show that the proposed algorithms can provide a convenient compromise in terms of computational demands and control authority.

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Le, Ankang. "Sensor based training optimization in professional cycling by model predictive control." Aachen Shaker, 2009. http://d-nb.info/1002144957/04.

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OKUMA, Shigeru, Tatsuya SUZUKI, YoungWoo KIM, and Tatsuya KATO. "Model Predictive Control of Traffic Flow Based on Hybrid System Modeling." Institute of Electronics, Information and Communication Engineers, 2005. http://hdl.handle.net/2237/14988.

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Ejegi, Eyefujirin Evans. "Model predictive based load frequency control studies in a deregulated environment." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/17112/.

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A fundamental objective in power system operations is to ensure reliablity and quality supply, and one key action that aids the accomplishment of this objective is the load frequency control (LFC). Primarily, LFC is an automatic action that aims to restore system frequency and net tie line power between a control area (CA) and its neighbours to their scheduled values; these quantities deviate when there is an imbalance between active power demand and supply in a synchrononus interconnection. This thesis aims to investigate a model predictive control (MPC) technique for LFC problems in a deregulated power system environment which has become a challenging task. In deregulated power interconnections, generation companies (GenCos) and distribution companies (DisCos) exist in each CA, and a transmission system operator (TSO) in each area is responsible for grid reliability. Each TSO handles LFC in its CA and ensures that market participants (GenCos and DisCos) in other CAs have an unbiased and open access to its network. As a result, there has been a rise in cross-border transac- tions between GenCos and DisCos for bulk energy and load matching (LM) and consequently large frequency fluctuations recently. DisCos can participate in LFC by making bilateral LM contracts with GenCos. An extensive review of the LFC literature, in terms of strengths and weaknesses of different control techniques, is presented to identify the key gaps. The review reveals that MPC can bring some benefits in the deregulated environment but its strengths are underexploited. Beginning with a small-scale system to provide insights into deregulated system modelling and predictive control design, a centralised MPC (CMPC)-based LFC scheme is proposed for a 2-area deregulated power system with measured (contracted) and unmeasured (uncontracted) load changes, where the areas are assumed to equally rated. The 2-area deregulated system is developed by incorporating bilateral LM contracts in the well known traditional LFC model as a new set of information. It is assumed that DisCos handle contracted load changes via bilateral LM contracts with GenCos and a TSO handles any variations outside the LM con- tracts (uncontracted) via a supplementary control scheme which represents the CMPC. The CMPC algorithm is developed as a tracking one, with an observer to provide estimates of the system states and uncontracted load changes. Also, input and incremental state constraints, which depict limits on LFC control efforts and generation rate constraints (GRC) respectively, are considered. A simulation comparison of the proposed CMPC solution and optimal linear quadratic regulator (LQR) demonstrates the efficacy of CMPC. Developing deregulated LFC models for larger systems with complex topologies and a large number of CAs/market participants could be laborious. Therefore, a novel generalised modelling framework for deregulated LFC is further proposed. The key benefits of the generalised framework is that it provides a relatively easy and systematic procedure to develop deregulated LFC benchmark systems irrespective of the interconnection size, topology and number of market participants. It also offers the flexibility of accommodating LFC studies where CAs have either equal (often assumed) or unequal (more pragmatic) rated capacities. A 7-area deregulated benchmark model is developed from the generalised framework to illustrate its usage and significance, and the importance of incorporating area rated capacities is demonstrated via simulations. In addition, a 4-area benchmark model is developed to provide a reader with more insight into how the generalised formulation can be applied to develop LFC models for an arbitrary network. Furthermore, to demonstrate the scalability of an MPC design procedure, the CMPC proposed previously is extended to examine the LFC problem of the 7-area system. Key novelties here are CAs are assumed to have unequal rated capacities, some GenCos do not participate in supplementary control, and the control input to each GenCo is computed separately rather than a single lumped input for each CA which is the norm in previous deregulated LFC studies. The separate control inputs is to ensure that the input constraints of each GenCo is accounted for in the CMPC in addition to their GRCs and this is achieved by incorporating the area participation factors of the GenCos explicitly in the CMPC cost function. A test conducted on the 7-area benchmark confirms the benefits of this new approach. CMPC shows great potential for deregulated LFC in terms of multiple inputs coordination, effective disturbance rejection and constraints handling; however it is unrealistic for practical interconnections were CAs are operated by different organisations and have large geographical separations. This limitation is addressed by investigating a distributed MPC (DMPC) technique for rejecting incremental load changes, convenient for a finite number of control areas (subsystems), and therefore represents a more practical control architecture for LFC in multi-area systems. The proposed DMPC is non-cooperative and developed to operate using output feedback, where distributed observers using local measurements are developed to provide uncontracted load changes and subsystem states’ estimates to local MPCs. Moreover, the DMPC, unlike other non-cooperative schemes, is simple and devoid of extensive offline parameter tuning. Using the 4-area and the 7-area benchmarks models developed as test systems for the proposed DMPC, some comparisons of simulations results, regulation cost and discussions are provided between the proposed DMPC and alternative MPC schemes.

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Xu, Shuqi. "Learning Model Predictive Control for Autonomous Racing : Improvements and Model Variation in Model Based Controller." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-247881.

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In this work, an improved Learning Model Predictive Control (LMPC)architecture for autonomous racing is presented. The controller is referencefree and is able to improve lap time by learning from history data of previouslaps. A terminal cost and a sampled safe set are learned from history data toguarantee recursive feasibility and non-decreasing performance at each lap.Improvements have been proposed to implement LMPC on autonomousracing in a more efficient and reliable way. Improvements have been doneon three aspects. Firstly, system identification has been improved to be runin a more efficient way by collecting feature data in subspace, so that thesize of feature data set is reduced and time needed to run sorting algorithmcan be reduced. Secondly, different strategies have been proposed toimprove model accuracy, such as least mean square with/without lifting andGaussian process regression. Thirdly, for reducing algorithm complexity,methods combining different model construction strategies were proposed.Also, running controller in a multi-rate way has also been proposed toreduced algorithm complexity when increment of controller frequency isnecessary. Besides, the performance of different system identificationstrategies have been compared, which include strategy from newton’s law,strategy from classical system identification and strategy from machinelearning. Factors that can possibly influence converged result of LMPCwere also investigated, such as prediction horizon, controller frequency.Experiment results on a 1:10 scaled RC car illustrates the effectiveness ofproposed improvements and the difference of different system identificationstrategies.
I detta arbete, presenteras en förbättrad inlärning baserad modell prediktivkontroll (LMPC) för autonom racing, styralgoritm är referens fritt och har visatsig att kunna förbättra varvtid genom att lära sig ifrån historiska data från tidigarevarv. En terminal kostnad och en samplad säker mängd är lärde ifrån historiskdata för att garantera rekursiv genomförbarhet och icke-avtagande prestanda vidvarje varv.förbättringar har presenterats för implementering av LMPC på autonom racingpå ett mer effektivt och pålitligt sätt. Förbättringar har gjorts på tre aspekter.Först, för system identifiering, föreslår vi att samlar feature data i delrummet,så att storlek på samlade datamängd reduceras och tiden som krävs för attköra sorteringsalgoritm minskas. För det andra, föreslår vi olika strategierför förbättrade modellnoggrannheten, såsom LMS med/utan lyft och Gaussianprocess regression. För det tredje, För att reducerar komplexitet för algoritm,metoder som kombinerar olika modellbygg strategier föreslogs. Att körastyrenhet på ett multi-rate sätt har också föreslagits till för att reduceraalgoritmkomplexitet då inkrementet av styrfrekvensen är nödvändigt.Prestanda av olika systemidentifiering har jämförts, bland annat, Newtonslag, klassisk systemidentifierings metoder och strategier från maskininlärning.Faktorer som eventuellt kan påverka konvergens av LMPC resultat har ocksåundersökts. Såsom, prediktions horisont, styrfrekvensen.Experimentresultat på en 1:10 skalad RC-bilen visar effektiviteten hos föreslagnaförbättringarna och skillnaderna i olika systemidentifierings strategier.

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Wredh, Simon. "Neural Network Based Model Predictive Control of Turbulent Gas-Solid Corner Flow." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420056.

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Over the past decades, attention has been brought to the importance of indoor air quality and the serious threat of bio-aerosol contamination towards human health. A novel idea to transport hazardous particles away from sensitive areas is to automatically control bio-aerosol concentrations, by utilising airflows from ventilation systems. Regarding this, computational fluid dynamics (CFD) may be employed to investigate the dynamical behaviour of airborne particles, and data-driven methods may be used to estimate and control the complex flow simulations. This thesis presents a methodology for machine-learning based control of particle concentrations in turbulent gas-solid flow. The aim is to reduce concentration levels at a 90 degree corner, through systematic manipulation of underlying two-phase flow dynamics, where an energy constrained inlet airflow rate is used as control variable. A CFD experiment of turbulent gas-solid flow in a two-dimensional corner geometry is simulated using the SST k-omega turbulence model for the gas phase, and drag force based discrete random walk for the solid phase. Validation of the two-phase methodology is performed against a backwards facing step experiment, with a 12.2% error correspondence in maximum negative particle velocity downstream the step. Based on simulation data from the CFD experiment, a linear auto-regressive with exogenous inputs (ARX) model and a non-linear ARX based neural network (NN) is used to identify the temporal relationship between inlet flow rate and corner particle concentration. The results suggest that NN is the preferred approach for output predictions of the two-phase system, with roughly four times higher simulation accuracy compared to ARX. The identified NN model is used in a model predictive control (MPC) framework with linearisation in each time step. It is found that the output concentration can be minimised together with the input energy consumption, by means of tracking specified target trajectories. Control signals from NN-MPC also show good performance in controlling the full CFD model, with improved particle removal capabilities, compared to randomly generated signals. In terms of maximal reduction of particle concentration, the NN-MPC scheme is however outperformed by a manually constructed sine signal. In conclusion, CFD based NN-MPC is a feasible methodology for efficient reduction of particle concentrations in a corner area; particularly, a novel application for removal of indoor bio-aerosols is presented. More generally, the results show that NN-MPC may be a promising approach to turbulent multi-phase flow control.

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Jing, Junbo. "Vehicle Fuel Consumption Optimization using Model Predictive Control based on V2V communication." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406201257.

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Benner, Peter, and Sabine Hein. "Model predictive control based on an LQG design for time-varying linearizations." Universitätsbibliothek Chemnitz, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-201000221.

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We consider the solution of nonlinear optimal control problems subject to stochastic perturbations with incomplete observations. In particular, we generalize results obtained by Ito and Kunisch in [8] where they consider a receding horizon control (RHC) technique based on linearizing the problem on small intervals. The linear-quadratic optimal control problem for the resulting time-invariant (LTI) problem is then solved using the linear quadratic Gaussian (LQG) design. Here, we allow linearization about an instationary reference trajectory and thus obtain a linear time-varying (LTV) problem on each time horizon. Additionally, we apply a model predictive control (MPC) scheme which can be seen as a generalization of RHC and we allow covariance matrices of the noise processes not equal to the identity. We illustrate the MPC/LQG approach for a three dimensional reaction-diffusion system. In particular, we discuss the benefits of time-varying linearizations over time-invariant ones.

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Hernandez, German Ardul Munoz. "Application of model based predictive control to a pumped storage hydroelectric plant." Thesis, Bangor University, 2005. https://research.bangor.ac.uk/portal/en/theses/application-of-model-based-predictive-control-to-a-pumped-storage-hydroelectric-plant(297cbcf2-0fdb-4f9a-9f84-5f43a1052606).html.

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This thesis describes the development of a Predictive Control to SISO and multivariable linear and nonlinear models of Dinorwig pumped storage hydroelectric power station. The results show that Generalised Predictive Control (GPC) offers significantly better performance across the plant's operating range when compared with classic PI controllers. The GPC controller produces a faster response when the station is operating with a single unit while preserving stability as the operating conditions change when multiple units are on-line. Inclusion of constraints in the GPC controller yields a fast, well-damped response in the common case when only a single Unit is in operation, without compromising stability when multiple Units are on-line. Simulation has also shown that improved power delivery is obtained when the plant is operated in frequency control mode. In the final part of the work a Mixed Logical Dynamical (MLD) predictive control was developed and applied to a MIMO nonlinear elastic model of Dinorwig. The results show that MLD predictive control is faster and less sensitive than the constrained GPC. The MLD predictive control can also be integrated with high-level plant functions.

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AL_Sheakh, Ameen Nael [Verfasser]. "Programming and Industrial Control, Model-Based Predictive Control of 3-Level Inverters / Nael AL_Sheakh Ameen." Wuppertal : Universitätsbibliothek Wuppertal, 2012. http://d-nb.info/1022901303/34.

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26

Walker, Jens. "A motion cueing model for mining and forestry simulator platforms based on Model Predictive Control." Thesis, Umeå universitet, Institutionen för fysik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-98685.

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Oryx Simulations produce simulators for mining and forestry machinery used for educational and promotional purposes. The simulators use motion platforms to reflect how the vehicle moves within the simulator. This platform tilts and accelerates the driver in order to enhance the experience. Previously a classical washout filter algorithm has been used to control the platform which leaves something to be desired regarding how well it reflects the vehicles movement, how easy it is to tune and how it handles the limits of the platform. This thesis aims to produce a model that accurately reflects angles, velocities and accelerations while in the mean time respecting the limits of the platform. In addition to this the developed model should be easy to modify and tune. This is achieved using so-called Model Predictive Control which achieves the wanted behaviour by predicting how the platform will move based on its current state while implementing the constraints of the platform directly into the model. Since all of the parameters in the model are actual physical quantities, this makes the model easier to tune. A key component in this solution is the so-called tilt coordination which consists of substituting a lateral/longitudinal acceleration with the acceleration of gravity by tilting the driver. Constructing and implementing this model in Matlab we verify it by using data extracted from the simulator environment. We see that the parameters consisting of angles, rotational velocities and linear accelerations are tracked very well while respecting the constraints for the platform, constraints that can be easily changed to fit the current simulator.We also see that the model successfully implements tilt coordination into the behaviour of the platform. This model performs extraordinarily well in theory, what remains is to implement this to the motion platform and fine-tune it.
Oryx Simulations tillverkar simulatorer i huvudsak för gruv- och skogsindustrinvilket används i utbildnings- och marknadsföringssyfte. Simulatorerna använder en röorelseplattform för att spegla hur fordonet i simulatormiljön rör sig. Denna plattform lutar och accelererar föraren för att förstarka upplevelsen. Tidigare har ett så kallat klassiskt washout-filter använts för att kontrollera plattformen som lämnar en del i övrigt att onska vad gäller hur väl fordonets rörelser speglas, hur lätt det ar att justera samt hur det hanterar plattformens begränsningar. Detta projekt ämnar producera en modell som väl speglar vinklar,hastigheter och accelerationer samtidigt som den respekterar plattformens gränser. I tillägg till detta bör modellen vara enkel att modifiera och justera. Detta uppnås genom så kallad Model Predictive Control som förutsager hur plattformen kommer röra sig utifrån dess aktuella tillstånd samtidigt som den respekterar de tvång som finns på plattformen direkt i modellen. Då alla parametrar i modellen är faktiska fysiska kvantiteter blir modellen märkbart lättare att justera. En viktig komponent i denna lösning är så kallad tilt coordination vilket består i att substituera lateral/longtudinell acceleration med en komposant av tyngdaccelerationen genom att luta föraren. Denna modell konstrueras och implementeras i Matlab och verifieras genom att använda extraherat data från den simulerade miljön. Vi kan se att parametrarna som består av vinklar, rotationella hastigheter och linjära accelerationer speglas väldigt väl, samtidigt som tvången på plattformen respekteras. Dessa tvång kan enkelt modieras for att passa den aktuella simulatorn. Vi ser även att modellen framgångsrikt implementerar tilt coordination i plattformens beteende. I teorin har denna modell väldigt bra prestanda; vad som kvarstår är att implementera den på en rörelseplattform och finjustera modellen.

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Varutti, Paolo [Verfasser]. "Model Predictive Control for Nonlinear Networked Control Systems : A Model-based Compensation Approach for Nondeterministic Communication Networks / Paolo Varutti." Aachen : Shaker, 2014. http://d-nb.info/1053361688/34.

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Chen, Xiao. "Fuel optimal powertrain control of heavy-duty vehicle based on model predictive control and quadratic programming." Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217527.

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The freight transport has a fundamental role in the world’s economic development.Due to the flexibility of heavy-duty vehicles, a large part of freighttransport is carried out inland. Although the use of heavy-duty vehicles contributesto the economic growth, the increased fuel consumption and globalgreenhouse gas emission that come with it constantly challenge the transportationsector to adapt and develop more fuel-efficient methods to reduce suchside effects while fulfilling the transportation requirements.This thesis considers fuel-optimal highway driving for heavy-duty vehicles.A model predictive control algorithm for minimizing fuel consumptionwhile satisfying constraints on desired speed is developed and evaluated. Thecontroller uses the available topography information of the road ahead of thevehicle in order to achieve an efficient vehicle control while satisfying a certaintrip time requirement. Under the assumption of fixed gear during the drivemission, the actual nonlinear problem is re-formulated as a real-time optimalcontrol problem based on MPC theory with a quadratic cost function and linearconstraints at each receding horizon of the drive mission. The QP problem isthen solved online and the resulting first control action is applied to the vehiclefor forward movement.The feasibility to implement such an algorithm on a control unit with limitedcomputational power is investigated and shown to be possible. Both therequirement of low computational complexity and low memory occupation arefulfilled by the tailored quadratic programming algorithm developed in thisthesis. The algorithm is fast enough to provide a solution within each samplinginterval.The overall control algorithm is implemented on a G5 control unit andtested in real life with a Scania truck during highway driving test. The resultsfrom both the real implementation and extensive simulations indicate that themethod provides a fuel-efficient vehicle behavior and is competitive with a rulebasedcontroller.
Transport av gods har en grundläggande roll i världens ekonomiska utveckling.På grund av flexibiliteten hos tunga fordon, utförs en stor del av allgodstransport med hjälp av dem. Trots att användning av tunga fordon bidrartill ekonomisk tillväxt, utgör bränsleförbrukning tillsammans med den ökadeutsläpp av växthusgas en utmaning för transportföretag att anpassa och utvecklamer bränslesnål och miljövänligare transportteknologi för tunga fordon.I detta examensarbete fokuserar man på körningen av lastbil på motorvägar.En bränsle optimal förutsägande styralgoritm är utvecklad och utvärderad.Algoritmen utnyttjar framför allt topografi information om vägen framför fordonetså att den kan planera körningen på ett bränslesparande sätt samtidigtsom den uppfyller ett visst tidskrav. Med antagande om konstant växel underkörningen, formuleras ett optimal styrningsproblem baserat på ett MPC ramverkmed kvadratisk målfunktion och linjära bivillkor. Den slutliga kvadratiskoptimeringsproblemet för varje styrhorisont är löst med hjälp av en för ändamåletframtagen QP-algoritm.Möjligheten att implementera en sådan algoritm på en inbyggd styrenhetär undersökt och veriferad. Både krav på låg beräkningskomplexitet och lågminnes användning är uppfylls av den MPC-anpassade QP-lösare som utvecklatsi detta examensarbete.Den slutliga styralgoritmen testades i verkligheten med en Scania lastbilpå motorväg. Resultat från både provkörning och simulering visar att metodenger en bränsleeffektiv körstrategi, som kan spara bränsle jämfört med en regelbaseradprediktiv farthållaren.

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Karimi, Pour Fatemeh. "Health-aware predictive control schemes based on industrial processes." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2020. http://hdl.handle.net/10803/673045.

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The research is motivated by real applications, such as pasteurization plant, water networks and autonomous system, which each of them require a specific control system to provide proper management able to take into account their particular features and operating limits in presence of uncertainties related to their operation and failures from component breakdowns. According to that most of the real systems have nonlinear behaviors, it can be approximated them by polytopic linear uncertain models such as Linear Parameter Varying (LPV) and Takagi-Sugeno (TS) models. Therefore, a new economic Model Predictive Control (MPC) approach based on LPV/TS models is proposed and the stability of the proposed approach is certified by using a region constraint on the terminal state. Besides, the MPC-LPV strategy is extended based on the system with varying delays affecting states and inputs. The control approach allows the controller to accommodate the scheduling parameters and delay change. By computing the prediction of the state variables and delay along a prediction time horizon, the system model can be modified according to the evaluation of the estimated state and delay at each time instant. To increase the system reliability, anticipate the appearance of faults and reduce the operational costs, actuator health monitoring should be considered. Regarding several types of system failures, different strategies are studied for obtaining system failures. First, the damage is assessed with the rainflow-counting algorithm that allows estimating the component’s fatigue and control objective is modified by adding an extra criterion that takes into account the accumulated damage. Besides, two different health-aware economic predictive control strategies that aim to minimize the damage of components are presented. Then, economic health-aware MPC controller is developed to compute the components and system reliability in the MPC model using an LPV modeling approach and maximizes the availability of the system by estimating system reliability. Additionally, another improvement considers chance-constraint programming to compute an optimal list replenishment policy based on a desired risk acceptability level, managing to dynamically designate safety stocks in flowbased networks to satisfy non-stationary flow demands. Finally, an innovative health-aware control approach for autonomous racing vehicles to simultaneously control it to the driving limits and to follow the desired path based on maximization of the battery RUL. The proposed approach is formulated as an optimal on-line robust LMI based MPC driven from Lyapunov stability and controller gain synthesis solved by LPV-LQR problem in LMI formulation with integral action for tracking the trajectory.
Esta tesis pretende proporcionar contribuciones teóricas y prácticas sobre seguridad y control de sistemas industriales, especialmente en la forma maten ática de sistemas inciertos. La investigación está motivada por aplicaciones reales, como la planta de pasteurización, las redes de agua y el sistema autónomo, cada uno de los cuales requiere un sistema de control específico para proporcionar una gestión adecuada capaz de tener en cuenta sus características particulares y limites o de operación en presencia de incertidumbres relacionadas con su operación y fallas de averías de componentes. De acuerdo con que la mayoría de los sistemas reales tienen comportamientos no lineales, puede aproximarse a ellos mediante modelos inciertos lineales politopicos como los modelos de Lineal Variación de Parámetros (LPV) y Takagi-Sugeno (TS). Por lo tanto, se propone un nuevo enfoque de Control Predictivo del Modelo (MPC) económico basado en modelos LPV/TS y la estabilidad del enfoque propuesto se certifica mediante el uso de una restricción de región en el estado terminal. Además, la estrategia MPC-LPV se extiende en función del sistema con diferentes demoras que afectan los estados y las entradas. El enfoque de control permite al controlador acomodar los parámetros de programación y retrasar el cambio. Al calcular la predicción de las variables de estado y el retraso a lo largo de un horizonte de tiempo de predicción, el modelo del sistema se puede modificar de acuerdo con la evaluación del estado estimado y el retraso en cada instante de tiempo. Para aumentar la confiabilidad del sistema, anticipar la aparición de fallas y reducir los costos operativos, se debe considerar el monitoreo del estado del actuador. Con respecto a varios tipos de fallas del sistema, se estudian diferentes estrategias para obtener fallas del sistema. Primero, el daño se evalúa con el algoritmo de conteo de flujo de lluvia que permite estimar la fatiga del componente y el objetivo de control se modifica agregando un criterio adicional que tiene en cuenta el daño acumulado. Además, se presentan dos estrategias diferentes de control predictivo económico que tienen en cuenta la salud y tienen como objetivo minimizar el daño de los componentes. Luego, se desarrolla un controlador MPC económico con conciencia de salud para calcular los componentes y la confiabilidad del sistema en el modelo MPC utilizando un enfoque de modelado LPV y maximiza la disponibilidad del sistema mediante la estimación de la confiabilidad del sistema. Además, otra mejora considera la programación de restricción de posibilidades para calcular una política ´optima de reposición de listas basada en un nivel de aceptabilidad de riesgo deseado, logrando designar dinámicamente existencias de seguridad en redes basadas en flujo para satisfacer demandas de flujo no estacionarias. Finalmente, un enfoque innovador de control consciente de la salud para vehículos de carreras autónomos para controlarlo simultáneamente hasta los límites de conducción y seguir el camino deseado basado en la maximización de la bacteria RUL. El diseño del control se divide en dos capas con diferentes escalas de tiempo, planificador de ruta y controlador. El enfoque propuesto está formulado como un MPC robusto en línea optimo basado en LMI impulsado por la estabilidad de Lyapunov y la síntesis de ganancia del controlador resuelta por el problema LPV-LQR en la formulación de LMI con acción integral para el seguimiento de la trayectoria.

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McNeilly, Gordon. "Coordinated control of hot strip tandem rolling mill." Thesis, University of Strathclyde, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366772.

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Grosso, Pérez Juan Manuel. "On model predictive control for economic and robust operation of generalised flow-based networks." Doctoral thesis, Universitat Politècnica de Catalunya, 2015. http://hdl.handle.net/10803/288218.

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This thesis is devoted to design Model Predictive Control (MPC) strategies aiming to enhance the management of constrained generalised flow-based networks, with special attention to the economic optimisation and robust performance of such systems. Several control schemes are developed in this thesis to exploit the available economic information of the system operation and the disturbance information obtained from measurements and forecasting models. Dynamic network flows theory is used to develop control-oriented models that serve to design MPC controllers specialised for flow networks with additive disturbances and periodically time-varying dynamics and costs. The control strategies developed in this thesis can be classified in two categories: centralised MPC strategies and non-centralised MPC strategies. Such strategies are assessed through simulations of a real case study: the Barcelona drinking water network (DWN). Regarding the centralised strategies, different economic MPC formulations are first studied to guarantee recursive feasibility and stability under nominal periodic flow demands and possibly time-varying economic parameters and multi-objective cost functions. Additionally, reliability-based MPC, chance-constrained MPC and tree-based MPC strategies are proposed to address the reliability of both the flow storage and the flow transportation tasks in the network. Such strategies allow to satisfy a customer service level under future flow demand uncertainty and to efficiently distribute overall control effort under the presence of actuators degradation. Moreover, soft-control techniques such as artificial neural networks and fuzzy logic are used to incorporate self-tuning capabilities to an economic certainty-equivalent MPC controller. Since there are objections to the use of centralised controllers in large-scale networks, two non-centralised strategies are also proposed. First, a multi-layer distributed economic MPC strategy of low computational complexity is designed with a control topology structured in two layers. In a lower layer, a set of local MPC agents are in charge of controlling partitions of the overall network by exchanging limited information on shared resources and solving their local problems in a hierarchical-like fashion. Moreover, to counteract the loss of global economic information due to the decomposition of the overall control task, a coordination layer is designed to influence non-iteratively the decision of local controllers towards the improvement of the overall economic performance. Finally, a cooperative distributed economic MPC formulation based on a periodic terminal cost/region is proposed. Such strategy guarantees convergence to a Nash equilibrium without the need of a coordinator and relies on an iterative and global communication of local controllers, which optimise in parallel their control actions but using a centralised model of the network.
Esta tesis se enfoca en el diseño de estrategias de control predictivo basado en modelos (MPC, por sus siglas en inglés) con la meta de mejorar la gestión de sistemas que pueden ser descritos por redes generalizadas de flujo y que están sujetos a restricciones, enfatizando especialmente en la optimización económica y el desempeño robusto de tales sistemas. De esta manera, varios esquemas de control se desarrollan en esta tesis para explotar tanto la información económica disponible de la operación del sistema como la información de perturbaciones obtenida de datos medibles y de modelos de predicción. La teoría de redes dinámicas de flujo es utilizada en esta tesis para desarrollar modelos orientados a control que sirven para diseñar controladores MPC especializados para la gestión de redes de flujo que presentan tanto perturbaciones aditivas como dinámicas y costos periódicamente variables en el tiempo. Las estrategias de control propuestas en esta tesis se pueden clasificar en dos categorías: estrategias de control MPC centralizado y estrategias de control MPC no-centralizado. Dichas estrategias son evaluadas mediante simulaciones de un caso de estudio real: la red de transporte de agua potable de Barcelona en España. En cuanto a las estrategias de control MPC centralizado, diferentes formulaciones de controladores MPC económicos son primero estudiadas para garantizar factibilidad recursiva y estabilidad del sistema cuya operación responde a demandas nominales de flujo periódico, a parámetros económicos posiblemente variantes en el tiempo y a funciones de costo multi-objetivo. Adicionalmente, estrategias de control MPC basado en fiabilidad, MPC con restricciones probabilísticas y MPC basado en árboles de escenarios son propuestas para garantizar la fiabilidad tanto de tareas de almacenamiento como de transporte de flujo en la red. Tales estrategias permiten satisfacer un nivel de servicio al cliente bajo incertidumbre en la demanda futura, así como distribuir eficientemente el esfuerzo global de control bajo la presencia de degradación en los actuadores del sistema. Por otra parte, técnicas de computación suave como redes neuronales artificiales y lógica difusa se utilizan para incorporar capacidades de auto-sintonía en un controlador MPC económico de certeza-equivalente. Dado que hay objeciones al uso de control centralizado en redes de gran escala, dos estrategias de control no-centralizado son propuestas en esta tesis. Primero, un controlador MPC económico distribuido de baja complejidad computacional es diseñado con una topología estructurada en dos capas. En una capa inferior, un conjunto de controladores MPC locales se encargan de controlar particiones de la red mediante el intercambio de información limitada de los recursos físicos compartidos y resolviendo sus problemas locales de optimización de forma similar a una secuencia jerárquica de solución. Para contrarrestar la pérdida de información económica global que ocurra tras la descomposición de la tarea de control global, una capa de coordinación es diseñada para influenciar no-iterativamente la decisión de los controles locales con el fin de lograr una mejora global del desempeño económico. La segunda estrategia no-centralizada propuesta en esta tesis es una formulación de control MPC económico distribuido cooperativo basado en una restricción terminal periódica. Tal estrategia garantiza convergencia a un equilibrio de Nash sin la necesidad de una capa de coordinación pero requiere una comunicación iterativa de información global entre todos los controladores locales, los cuales optimizan en paralelo sus acciones de control utilizando un modelo centralizado de la red.

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Choi, Rejina Ling Wei. "Modelling and Model Based Control Design For Rotorcraft Unmanned Aerial Vehicle." Thesis, University of Canterbury. Mechanical Engineering, 2014. http://hdl.handle.net/10092/9933.

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Designing high performance control of rotorcraft unmanned aerial vehicle (UAV) requires a mathematical model that describes the dynamics of the vehicle. The model is derived from first principle modelling, such as rigid-body dynamics, actuator dynamics and etc. It is found that simplified decoupled model of RUAV has slightly better data fitting compared with the complex model for helicopter attitude dynamics in hover or near hover flight condition. In addition, the simplified modelling approach has made the analysis of system dynamics easy. System identification method is applied to identify the unknown intrinsic parameters in the nominal model, where manual piloted flight experiment is carried out and input-output data about a nominal operating region is recorded for parameters identification process. Integral-based parameter identification algorithm is then used to identify model parameters that give the best matching between the simulation and measured output response. The results obtained show that the dominant dynamics is captured. The advantages of using integral-based method include the fast computation time, insensitive to initial parameter value and fast convergence rate in comparison with other contemporary system identification methods such as prediction error method (PEM), maximum likelihood method, equation error method and output error method. Besides, the integral-based parameter identification method can be readily extended to tackle slow time-varying model parameters and fast varying disturbances. The model prediction is found to be improved significantly when the iterative integral-based parameter identification is employed and thus further validates the minimal modelling approach. From the literature review, many control schemes have been designed and validated in simulation. However, few of them has really been implemented in real flight as well as under windy and severe conditions, where unpredictable large system parameters variations and unexpected disturbances are present. Therefore, the emphasis on this part will be on the control design that would have satisfactory reference sequence tracking or regulation capability in the presence of unmodelled dynamics and external disturbances. Generalised Predictive Controller (GPC) is particularly considered as the helicopter attitude dynamics control due to its insensitivity with respect to model mismatch and its capability to address the control problem of nominal model with deadtime. The robustness analysis shows that the robustness of the basic GPC is significantly improved using the Smith Predictor (SP) in place of optimal predictor in basic GPC. The effectiveness of the proposed robust GPC was well proven with the control of helicopter heading on the test rig in terms of the reference sequence tracking performance and the input disturbance rejection capability. The second motivation is the investigation of adaptive GPC from the perspective of performance improvements for the robust GPC. The promising experimental results prove the feasibility of the adaptive GPC controller, and especially evident when the underlying robust GPC is tuned with low robustness and legitimates the use of simplified model. Another approach of robust model predictive control is considered where disturbance is identified in real‐time using an iterative integral‐based method.

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Flood, Cecilia. "Real-time Trajectory Optimization for Terrain Following Based on Non-linear Model Predictive Control." Thesis, Linköping University, Department of Electrical Engineering, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1136.

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There are occasions when it is preferable that an aircraft flies asclose to the ground as possible. It is difficult for a pilot to predict the topography when he cannot see beyond the next hill, and this makes it hard for him to find the optimal flight trajectory. With the help of a terrain database in the aircraft, the forthcoming topography can be found in advance and a flight trajectory can be calculated in real-time. The main goal is to find an optimal control sequence to be used by the autopilot. The optimization algorithm, which is created for finding the optimal control sequence, has to be run often and therefore, it has to be fast.

This thesis presents a terrain following algorithm based on Model Predictive Control which is a promising and robust way of solving the optimization problem. By using trajectory optimization, a trajectory which follows the terrain very good is found for the non-linear model of the aircraft.

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Mechelli, Luca [Verfasser]. "POD-based State-Constrained Economic Model Predictive Control for Convection-Diffusion Phenomena / Luca Mechelli." Konstanz : KOPS Universität Konstanz, 2019. http://d-nb.info/1200355075/34.

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Human, Gerhardus. "Model based predictive control for load following of a pressurised water reactor / Gerhardus Human." Thesis, North-West University, 2009. http://hdl.handle.net/10394/4017.

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By September 2009 the International Atomic Energy Agency reported that the number of commercially operated nuclear reactors in 30 countries across the world is 436, around 50 reactors are currently being constructed, 137 reactors have been ordered or is already planned, and there are around 295 proposed reactors. Pressurised water reactors (PWRs) make up the majority of these numbers. The growing number of carbon emissions and the ongoing fight against fossil fuel power stations might see the number of planned nuclear reactors increase even more to be able to satisfy the world’s need for cleaner energy. To ensure that technology keeps pace with this growing demand, ongoing research is essential. Not only is the research of new reactor technologies (i.e. High Temperature Reactors) important, but improving the current technologies (i.e. PWRs) is critical. With the increased contribution of nuclear generated electricity to our grids, it is becoming more common for nuclear reactors to be operated as load following units, and not base load units as they are more commonly being operated. Therefore a need exists to study and develop new strategies and technologies to improve the automatic load following capabilities of reactors. PWR power plants are multivariable systems. In this study a multivariable, more specifically, a model predictive controller (MPC) is developed for controlling the load following of a nuclear power plant, more specifically a PWR plant. In developing this controller system identification is employed to develop a model of the PWR plant. For the identification of the model, measured data from a computer based PWR simulator is used as the input. The identified plant model is used to develop the MPC controller. The controller is developed and tested on the plant model. The MPC controller is also evaluated against another set of measured data from the simulator. To compare the performance of the MPC controller to that of the conventional controller the ITAE performance index is employed. During the process Matlab ® , the System Identification Toolbox™, the MPC Toolbox™ and Simulink ® are used. The results reveal that MPC is practicable to be used in the control of non-linear systems such as PWR plants. The MPC controller showed good results for controlling the system and also outperformed the conventional controllers. A further result from the dissertation is that system identification can successfully be used to develop models for use in model based controllers like MPC controllers. The results of the research show that a need exists for future research to improve the methods to eventually have a controller that can be applied on a commercial plant.
Thesis (M.Ing. (Nuclear Engineering))--North-West University, Potchefstroom Campus, 2010.

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Rogers, Andrew Charles. "Optimization-Based Guidance for Satellite Relative Motion." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/79455.

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Spacecraft relative motion modeling and control promises to enable or augment a wide range of missions for scientific research, military applications, and space situational awareness. This dissertation focuses on the development of novel, optimization-based, control design for some representative relative-motion-enabled missions. Spacecraft relative motion refers to two (or more) satellites in nearly identical orbits. We examine control design for relative configurations on the scale of meters (for the purposes of proximity operations) as well as on the scale of tens of kilometers (representative of science gathering missions). Realistic control design for satellites is limited by accurate modeling of the relative orbital perturbations as well as the highly constrained nature of most space systems. We present solutions to several types of optimal orbital maneuvers using a variety of different, realistic assumptions based on the maneuver objectives. Initially, we assume a perfectly circular orbit with a perfectly spherical Earth and analytically solve the under-actuated, minimum-energy, optimal transfer using techniques from optimal control and linear operator theory. The resulting open-loop control law is guaranteed to be a global optimum. Then, recognizing that very few, if any, orbits are truly circular, the optimal transfer problem is generalized to the elliptical linear and nonlinear systems which describe the relative motion. Solution of the minimum energy transfer for both the linear and nonlinear systems reveals that the resulting trajectories are nearly identical, implying that the nonlinearity has little effect on the relative motion. A continuous-time, nonlinear, sliding mode controller which tracks the linear trajectory in the presence of a higher fidelity orbit model shows that the closed-loop system is both asymptotically stable and robust to disturbances and un-modeled dynamics. Next, a novel method of computing discrete-time, multi-revolution, finite-thrust, fuel-optimal, relative orbit transfers near an elliptical, perturbed orbit is presented. The optimal control problem is based on the classical, continuous-time, fuel-optimization problem from calculus of variations, and we present the discrete-time analogue of this problem using a transcription-based method. The resulting linear program guarantees a global optimum in terms of fuel consumption, and we validate the results using classical impulsive orbit transfer theory. The new method is shown to converge to classical impulsive orbit transfer theory in the limit that the duration of the zero-order hold discretization approaches zero and the time horizon extends to infinity. Then the fuel/time optimal control problem is solved using a hybrid approach which uses a linear program to solve the fuel optimization, and a genetic algorithm to find the minimizing time-of-flight. The method developed in this work allows mission planners to determine the feasibility for realistic spacecraft and motion models. Proximity operations for robotic inspection have the potential to aid manned and unmanned systems in space situational awareness and contingency planning in the event of emergency. A potential limiting factor is the large number of constraints imposed on the inspector vehicle due to collision avoidance constraints and limited power and computational resources. We examine this problem and present a solution to the coupled orbit and attitude control problem using model predictive control. This control technique allows state and control constraints to be encoded as a mathematical program which is solved on-line. We present a new thruster constraint which models the minimum-impulse bit as a semi-continuous variable, resulting in a mixed-integer program. The new model, while computationally more expensive, is shown to be more fuel-efficient than a sub-optimal approximation. The result is a fuel efficient, trajectory tracking, model predictive controller with a linear-quadratic attitude regulator which tracks along a pre-computed ``safe'' trajectory in the presence of un-modeled dynamics on a higher fidelity orbital and attitude model.
Ph. D.

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Yu, Mingzhao. "Model Reduction and Nonlinear Model Predictive Control of Large-Scale Distributed Parameter Systems with Applications in Solid Sorbent-Based CO2 Capture." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/887.

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This dissertation deals with some computational and analytic challenges for dynamic process operations using first-principles models. For processes with significant spatial variations, spatially distributed first-principles models can provide accurate physical descriptions, which are crucial for offline dynamic simulation and optimization. However, the large amount of time required to solve these detailed models limits their use for online applications such as nonlinear model predictive control (NMPC). To cope with the computational challenge, we develop computationally efficient and accurate dynamic reduced order models which are tractable for NMPC using temporal and spatial model reduction techniques. Then we introduce an input and state blocking strategy for NMPC to further enhance computational efficiency. To improve the overall economic performance of process systems, one promising solution is to use economic NMPC which directly optimizes the economic performance based on first-principles dynamic models. However, complex process models bring challenges for the analysis and design of stable economic NMPC controllers. To solve this issue, we develop a simple and less conservative regularization strategy with focuses on a reduced set of states to design stable economic NMPC controllers. In this thesis, we study the operation problems of a solid sorbent-based CO2 capture system with bubbling fluidized bed (BFB) reactors as key components, which are described by a large-scale nonlinear system of partial-differential algebraic equations. By integrating dynamic reduced models and blocking strategy, the computational cost of NMPC can be reduced by an order of magnitude, with almost no compromise in control performance. In addition, a sensitivity based fast NMPC algorithm is utilized to enable the online control of the BFB reactor. For economic NMPC study, compared with full space regularization, the reduced regularization strategy is simpler to implement and lead to less conservative regularization weights. We analyze the stability properties of the reduced regularization strategy and demonstrate its performance in the economic NMPC case study for the CO2 capture system.

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Hayakawa, Yoshikazu, and Tomohiko Jimbo. "Model Predictive Control for Automotive Engine Torque Considering Internal Exhaust Gas Recirculation." International Federation of Automatic Control (IFAC), 2011. http://hdl.handle.net/2237/20769.

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Hitzemann, U. "Extensions in non-minimal state-space and state-dependent parameter model based control with application to a DC-DC boost converter." Thesis, Coventry University, 2013. http://curve.coventry.ac.uk/open/items/ca983ce5-bec4-4598-8ac2-48e7302489f5/1.

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This Thesis is concerned with model-based control, where models of linear nonminimal state-space (NMSS) and nonlinear state-dependent parameter (SDP) form are considered. In particular, the focus is on model-based predictive control (MPC) in conjunction with the linear NMSS model and on proportional-integralplus (PIP) pole-assignment control in conjunction with the SDP model. The SDP-PIP pole-assignment controller is based on a nonlinear SDP model, however, the approach uses a linear pole-assignment controller design technique. This ‘potential paradox’ is addressed in this Thesis. A conceptual approach to realising the SDP-PIP pole-assignment control is proposed, where an additional conceptual time-shift operator is introduced. This allows the SDPPIP, at each sampling time instance, to be considered as an equivalent linear controller, while operating, in fact, in a nonlinear overall context. Additionally, an attempt to realise SDP-PIP control, where the SDP model exhibits equivalent linear system numerator zeros, is proposed. Regarding the NMSS MPC, emphasis is on square, i.e. equal number of inputs and outputs, multi-input multi-output (MIMO) modelled systems, which exhibit system output cross-coupling effects. Moreover, the NMSS MPC in incremental input form and making use of an integral-of-errors state variable, is considered. A strategy is proposed, that allows decoupling of the system outputs by diagonalising the closed-loop system model via an input transformation. A modification to the NMSS MPC in incremental input form is proposed such that the transformed system input - system output pairs can be considered individually, which allows the control and prediction horizons to be assigned to the individual pairs separately. This modification allows imposed constraints to be accommodated such that the cross-coupling effects do not re-emerge. A practical example is presented, namely, a DC-DC boost converter operating in discontinuous conduction mode (DCM), for which a SDP model is developed. This model is based on measured input-output data rather than on physical relationships. The model incorporates the output current so that the requirements for the load, driven by the converter, is constrained to remain within a predefined output current range. The proposed SDP model is compared to an alternative nonlinear Hammerstein-bilinear structured (HBS) model. The HBS model is, in a similar manner to the SDP model, also based on measured input-output data. Moreover, the differences as well as the similarities of the SDP and HBS model are elaborated. Furthermore, SDP-PIP pole-assignment control, based on the developed SDP model, is applied to the converter and the performance is compared to baseline linear PIP control schemes.

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Arif, Bilal. "Real-time grid parameter estimation methods using model based predictive control for grid-connected converters." Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/31963/.

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In recent years, renewable and distributed generation (DG) systems have contributed towards an efficient and an economic way of transporting electricity to end-users as the generation sources are in general located nearer the loads. DG and renewable energy systems are modifying the old concept of distribution network by instigating a bi-directional power flow into the grid, facilitated through the use of power electronic grid-connected converters. A challenge associated with grid-connected converters arises when they are interacted with a grid that is not stiff, like weak micro grids. Small grid parameter variations in these systems can considerably affect the performance of the converter control and lead to higher values in current total harmonic distortion (THD) and loss of control and synchronization. Thus, the control of grid-connected power converters needs to be regularly updated with latest variation in grid parameters. Model Predictive Direct Power Control (MP-DPC) has been chosen as the control strategy for the work presented in this thesis due to its advantages over traditional control techniques such as multivariable control, no need of phase-locked loops (PLLs) for grid synchronization and avoidance of cascaded control loops. Two novel methods for estimating the grid impedance variation, and hence the grid voltage, are presented in this thesis along with a detailed literature review on control of grid-connected converters with special emphasis on impedance estimation techniques. The first proposed estimation method is based on the difference in grid voltage magnitudes at two consecutive sampling instants while the second method is based on a model-fitting algorithm similar to the concept of cost-function optimization in model predictive control. The proposed estimation methods in this thesis are integrated within the MP-DPC, therefore updating the MP-DPC in real-time with the latest variation in grid impedance. The proposed algorithms provide benefits such as: quick response to transient variations, operation under low values of short-circuit-ratio (SCR), robust MP-DPC control, good reference tracking to grid parameter variations and operation under unbalanced grid voltages. The thesis also presents the advantages and drawbacks of the proposed methods and areas where further improvement can be researched. The work presented has been tested on a three phase two-level grid-connected converter prototype, which is connected to a low voltage substation highly dominated by inductive component of grid impedance. It can be adapted and modified to be used for general grid impedance estimation, medium or high voltage applications, in case of multilevel grid-connected converter topologies or photo-voltaic (PV) grid-connected applications.

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Hoffmann, Kai [Verfasser]. "Non-linear model-based predictive control of a low-temperature gasoline combustion engine / Kai Hoffmann." Düsseldorf : VDI-Verl, 2010. http://d-nb.info/1005312478/34.

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Böck, Martin [Verfasser]. "Model Predictive and Flatness-based Path Following Control and Manifold Stabilization with Applications / Martin Böck." Aachen : Shaker, 2016. http://d-nb.info/1118258584/34.

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Käpernick, Bartosz [Verfasser]. "Gradient-based nonlinear model predictive control with constraint transformation for fast dynamical systems / Bartosz Käpernick." Ulm : Universität Ulm, 2016. http://d-nb.info/1166756491/34.

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Carneiro, Gustavo Lima. "Model based predictive control applied to the aircraft longitudinal mode for a terrain following task." Instituto Tecnológico de Aeronáutica, 2009. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=1228.

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In the present work, a study is proposed about the applicability of a predictive controller to be used to control the longitudinal mode of an aircraft. The objective is to evaluate the performance of such control approach applied to a terrain following task, verifying the tracking suitability while respecting physical constraints to which the aircraft is subjected to. As examples, control surfaces range limitations, restrictions for the available thrust as well as other variables such as the angle of attack, velocity, pitch rate and the altitude itself. A fighter aircraft simplified model was used for the longitudinal movement to perform the simulations. The predictive control approach used was based on a linear prediction model described in the state space. Therefore, it was necessary to linearize the aircraft dynamic around an equilibrium point previously chosen. Two scenarios were evaluated for the same terrain profile. The first considered the simulation with the system nominal constraints. The second scenario covered an elevator actuator failure, in order to analyze the suitability of such controller when dealing with the online imposed constraints. The advantages of the predictive control methodology were evident based on the results for both scenarios, where an adequate terrain profile tracking was observed and, at the same time, the imposed restrictions were enforced in the performed simulations.

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Käpernick, Bartosz Maciej [Verfasser]. "Gradient-based nonlinear model predictive control with constraint transformation for fast dynamical systems / Bartosz Käpernick." Ulm : Universität Ulm, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-8522-4.

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Li, Xiawen. "Power System Stability Improvement with Decommissioned Synchronous Machine Using Koopman Operator Based Model Predictive Control." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/102503.

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Traditional generators have been decommissioned or replaced by renewable energy generation due to utility long-standing goals. However, instead of flattening the entire plant, the rotating mass of generator can be utilized as a storage unit (inertia resource) to mitigate the frequency swings during transient caused by the renewables. The goal of this work is to design a control strategy utilizing the decommissioned generator interfaced with power grid via a back-to-back converter to provide inertia support. This is referred to as decoupled synchronous machine system (DSMS). On top of that, the grid-side converter is capable of providing reactive power as an auxiliary voltage controller. However, in a practical setting, for power utilities, the detailed state equations of such device as well as the complicated nonlinear power system are usually unobtainable making the controller design a challenging problem. Therefore, a model free, purely data-driven strategy for the nonlinear controller design using Koopman operator-based framework is proposed. Besides, the time delay embedding technique is adopted together with Koopman operator theory for the nonlinear system identification. Koopman operator provides a linear representation of the system and thereby the classical linear control algorithms can be applied. In this work, model predictive control is adopted to cope with the constraints of the control signals. The effectiveness and robustness of the proposed system are demonstrated in Kundur two-area system and IEEE 39-bus system.
Doctor of Philosophy

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Bahremand, Saeid. "Blood Glucose Management Streptozotocin-Induced Diabetic Rats by Artificial Neural Network Based Model Predictive Control." Thesis, Southern Illinois University at Edwardsville, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10249804.

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Diabetes is a group of metabolic diseases where the body’s pancreas does not produce enough insulin or does not properly respond to insulin produced, resulting in high blood sugar levels over a prolonged period. There are several different types of diabetes, but the most common forms are type 1 and type 2 diabetes. Type 1 diabetes Mellitus (T1DM) can occur at any age, but is most commonly diagnosed from infancy to late 30s. If a person is diagnosed with type 1 diabetes, their pancreas produces little to no insulin, and the body’s immune system destroys the insulin-producing cells in the pancreas. Those diagnosed with type 1 diabetes must inject insulin several times every day or continually infuse insulin through a pump, as well as manage their diet and exercise habits. If not treated appropriately, it can cause serious complications such as cardiovascular disease, stroke, kidney failure, foot ulcers, and damage to eyes.

During the past decade, researchers have developed artificial pancreas (AP) to ease management of diabetes. AP has three components: continuous glucose monitor (CGM), insulin pump, and closed-loop control algorithm. Researchers have developed algorithms based on control techniques such as Proportional Integral Derivative (PID) and Model Predictive Control (MPC) for blood glucose level (BGL) control; however, variability in metabolism between or within individuals hinders reliable control.

This study aims to develop an adaptive algorithm using Artificial Neural Networks (ANN) based Model Predictive Control (NN-MPC) to perform proper insulin injections according to BGL predictions in diabetic rats. This study is a ground work to implement NN-MPC algorithm on real subjects. BGL data collected from diabetic rats using CGM are used with other inputs such as insulin injection and meal information to develop a virtual plant model based on a mathematical model of glucose–insulin homeostasis proposed by Lombarte et al. Since this model is proposed for healthy rats; a revised version on this model with three additional equations representing diabetic rats is used to generate data for training ANN which is applicable for the identi?cation of dynamics and the glycemic regulation of rats. The trained ANN is coupled with MPC algorithm to control BGL of the plant model within the normal range of 100 to 130 mg/dl by injecting appropriate amount of insulin. The ANN performed well with less than 5 mg/dl error (2%) for 5-minute prediction and about 15 mg/dl error (7%) for 30-minute prediction. In ¬¬addition, the NN-MPC algorithm kept BGL of diabetic rats more than 90 percent of the time within the normal range without hyper/hypo-glycaemia.

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Lefort, Antoine. "A smart grid ready building energy management system based on a hierarchical model predictive control." Thesis, Supélec, 2014. http://www.theses.fr/2014SUPL0010/document.

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L’intégration des énergies renouvelables produites par un bâtiment et les réseaux de fourniture, qui sont amenés à proposer des tarifications et des puissances disponibles variables au cours de la journée, entraînent une grande variabilité de la disponibilité de l’énergie. Mais les besoins des utilisateurs ne sont pas forcément en accord avec cette disponibilité. La gestion de l’énergie consiste alors à faire en sorte que les moments de consommation des installations coïncident avec les moments où celle-ci est disponible. Notre objectif a été de proposer une stratégie de commande prédictive, distribuée et hiérarchisée, pour gérer efficacement l’énergie de l’habitat. Les aspects prédictifs de notre approche permettent d’anticiper les besoins et les variations de la tarification énergétique. L’aspect distribué va permettre d’assurer la modularité de la structure de commande, pour pouvoir intégrer différents usages et différentes technologies de manière simple et sans faire exploser la combinatoire du problème d’optimisation résultant
Electrical system is under a hard constraint: production and consumption must be equal. The production has to integrate non-controllable energy resources and to consider variability of local productions. While buildings are one of the most important energy consumers, the emergence of information and communication technologies (ICT) in the building integrates them in smart-grid as important consumer-actor players. Indeed, they have at their disposal various storage capacities: thermal storage, hot-water tank and also electrical battery. In our work we develop an hierarchical and distributed Building Energy Management Systems based on model predictive control in order to enable to shift, to reduce or even to store energy according to grid informations. The anticipation enables to plan the energy consumption in order to optimize the operating cost values, while the hierarchical architecture enables to treat the high resolution problem complexity and the distributed aspect enables to ensure the control modularity bringing adaptability to the controller

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GUIDOLINI, R. "A NEURAL-BASED MODEL PREDICTIVE CONTROL TO TACKLE STEERING DELAY OF THE IARA AUTONOMOUS CAR." Universidade Federal do Espírito Santo, 2017. http://repositorio.ufes.br/handle/10/9852.

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Neste trabalho, propomos uma abordagem de Controle Preditivo Baseado em Modelo Neural (Neural Based Model Predictive Control - N-MPC) para lidar com atrasos na planta de direção de carros autônomos. Examinamos a abordagem N-MPC como uma alternativa para a implementação do subsistema de controle de direção da Intelligent and Autonomous Robotic Automobile (IARA). Para isso, comparamos a solução padrão, baseada na abordagem de controle Proporcional Integral Derivativo (PID), com a abordagem N-MPC. O subsistema de controle de direção PID funciona bem na IARA para velocidades de até 25 km/h. No entanto, acima desta velocidade, atrasos na Planta de Direção da IARA são muito elevados para permitir uma operação adequada usando uma abordagem PID. Modelamos a Planta de Direção da IARA usando uma rede neural e empregamos esse modelo neural na abordagem N-MPC. A abordagem N-MPC superou a abordagem PID reduzindo o impacto de atrasos na Planta de Direção de IARA e permitindo a operação autônoma da IARA em velocidades de até 37 km/h um aumento de 48% na velocidade máxima estável

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Karlsson, Axel, and Bohan Zhou. "Model-Based versus Data-Driven Control Design for LEACH-based WSN." Thesis, KTH, Maskinkonstruktion (Inst.), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272197.

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In relation to the increasing interest in implementing smart cities, deployment of widespread wireless sensor networks (WSNs) has become a current hot topic. Among the application’s greatest challenges, there is still progress to be made concerning energy consumption and quality of service. Consequently, this project aims to explore a series of feasible solutions to improve the WSN energy efficiency for data aggregation by the WSN. This by strategically adjusting the position of the receiving base station and the packet rate of the WSN nodes. Additionally, the low-energy adaptive clustering hierarchy (LEACH) protocol is coupled with the WSN state of charge (SoC). For this thesis, a WSN was defined as a two dimensional area which contains sensor nodes and a mobile sink, i.e. a movable base station. Subsequent to the rigorous analyses of the WSN data clustering principles and system-wide dynamics, two different developing strategies, model-based and data-driven designs, were employed to develop two corresponding control approaches, model predictive control and reinforcement learning, on WSN energy management. To test their performance, a simulation environment was thus developed in Python, including the extended LEACH protocol. The amount of data transmitted by an energy unit is adopted as the index to estimate the control performance. The simulation results show that the model based controller was able to aggregate over 22% more bits than only using the LEACH protocol. Whilst the data driven controller had a worse performance than the LEACH network but showed potential for smaller sized WSNs containing a fewer amount of nodes. Nonetheless, the extension of the LEACH protocol did not give rise to obvious improvement on energy efficiency due to a wide range of differing results.
I samband med det ökande intresset för att implementera så kallade smart cities, har användningen av utbredda trådlösa sensor nätverk (WSN) blivit ett intresseområde. Bland applikationens största utmaningar, finns det fortfarande förbättringar med avseende på energiförbrukning och servicekvalité. Därmed så inriktar sig detta projekt på att utforska en mängd möjliga lösningar för att förbättra energieffektiviteten för dataaggregation inom WSN. Detta gjordes genom att strategiskt justera positionen av den mottagande basstationen samt paketfrekvensen för varje nod. Dessutom påbyggdes low-energy adaptive clustering hierarchy (LEACH) protokollet med WSN:ets laddningstillstånd. För detta examensarbete definierades ett WSN som ett två dimensionellt plan som innehåller sensor noder och en mobil basstation, d.v.s. en basstation som går att flytta. Efter rigorös analys av klustringsmetoder samt dynamiken av ett WSN, utvecklades två kontrollmetoder som bygger på olika kontrollstrategier. Dessa var en modelbaserad MPC kontroller och en datadriven reinforcement learning kontroller som implementerades för att förbättra energieffektiviteten i WSN. För att testa prestandan på dom två kontrollmetoderna, utvecklades en simulations platform baserat på Python, tillsamans med påbyggnaden av LEACH protokollet. Mängden data skickat per energienhet användes som index för att approximera kontrollprestandan. Simuleringsresultaten visar att den modellbaserade kontrollern kunde öka antalet skickade datapacket med 22% jämfört med när LEACH protokollet användes. Medans den datadrivna kontrollern hade en sämre prestanda jämfört med när enbart LEACH protokollet användes men den visade potential för WSN med en mindre storlek. Påbyggnaden av LEACH protokollet gav ingen tydlig ökning med avseende på energieffektiviteten p.g.a. en mängd avvikande resultat.

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