Relevant bibliographies by topics / Model-based predictive control (MBPC)

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  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Model-based predictive control (MBPC)'

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Published: 4 June 2021
Last updated: 25 January 2023

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Journal articles on the topic "Model-based predictive control (MBPC)"

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Bot, Karol, Inoussa Laouali, António Ruano, and Maria da Graça Ruano. "Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques." Energies 14, no. 18 (September 16, 2021): 5852. http://dx.doi.org/10.3390/en14185852.

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At a global level, buildings constitute one of the most significant energy-consuming sectors. Current energy policies in the EU and the U.S. emphasize that buildings, particularly those in the residential sector, should employ renewable energy and storage and efficiently control the total energy system. In this work, we propose a Home Energy Management System (HEMS) by employing a Model-Based Predictive Control (MBPC) framework, implemented using a Branch-and-Bound (BAB) algorithm. We discuss the selection of different parameters, such as time-step, to employ prediction and control horizons and the effect of the weather in the system performance. We compare the economic performance of the proposed approach against a real PV-battery system existing in a household equipped with several IoT devices, concluding that savings larger than 30% can be obtained, whether on sunny or cloudy days. To the best of our knowledge, these are excellent values compared with existing solutions available in the literature.
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Sendoya, Diego Fernando. "¿Qué es el Control Predictivo y Hacia Dónde se Proyecta?" Publicaciones e Investigación 7 (June 2, 2013): 53. http://dx.doi.org/10.22490/25394088.1106.

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<p align="left">El control predictivo basado en modelo (<em>Model Based Predictive Control – MBPC o simplemente MPC</em>) es una metodologia de control que hace uso del modelo del proceso para predecir las salidas futuras de la planta y con base en ello optimizar las acciones de control futuras. De hecho, el control predictivo no se puede considerar como una estrategia de control independiente sino, que por el contrario, integra toda una familia de metodos de control tales como, el control optimo, el control de procesos con tiempos muertos, el control de procesos multivariables, etc. Esto ha permitido que el control predictivo haya tenido un desarrollo importante tanto en la comunidad cientifica y academica, como en el sector industrial.</p>
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Merabti, Halim, and Khaled Belarbi. "Accelerated micro particle swarm optimization for the solution of nonlinear model predictive control." World Journal of Engineering 14, no. 6 (December 4, 2017): 509–21. http://dx.doi.org/10.1108/wje-01-2017-0004.

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Purpose Rapid solution methods are still a challenge for difficult optimization problems among them those arising in nonlinear model predictive control. The particle swarm optimization algorithm has shown its potential for the solution of some problems with an acceptable computation time. In this paper, we use an accelerated version of PSO for the solution of simple and multiobjective nonlinear MBPC for unmanned vehicles (mobile robots and quadcopter) for tracking trajectories and obstacle avoidance. The AµPSO-NMPC was applied to control a LEGO mobile robot for the tracking of a trajectory without and with obstacles avoidance one. Design/methodology/approach The accelerated PSO and the NMPC are used to control unmanned vehicles for tracking trajectories and obstacle avoidance. Findings The results of the experiments are very promising and show that AµPSO can be considered as an alternative to the classical solution methods. Originality/value The computation time is less than 0.02 ms using an Intel Core i7 with 8GB of RAM.
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Mahmoudi, Abdelkader, Imed Jlassi, Antonio J. Marques Cardoso, and Khaled Yahia. "Model Free Predictive Current Control Based on a Grey Wolf Optimizer for Synchronous Reluctance Motors." Electronics 11, no. 24 (December 13, 2022): 4166. http://dx.doi.org/10.3390/electronics11244166.

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A Model-based predictive current control (MBPCC) has recently become a powerful advanced control technology in industrial drives. However, MBPCC relies on the knowledge of the system model and parameters, being, therefore, very sensitive to parameters errors. In the case of the synchronous reluctance motor (SynRM), where the parameters vary due to its ferromagnetic structure and nonlinear magnetic properties, MBPCC performance would suffer significantly. Accordingly, in this paper, a Grey Wolf Optimizer based model-free predictive current control (GW-MFPCC) of SynRM is proposed, to skip all the effects of the model dependency and parameters uncertainty. The proposed method predicts the stator current through tracking the minimum cost function using the grey wolf optimizer. The proposed GW-MFPCC scheme is compared to MBPCC, and its effectiveness is evaluated and confirmed by experimental results.
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Donaisky, Emerson, Gustavo H. C. Oliveira, and Nathan Mendes. "Algoritmos PMV-MBPC para conforto térmico em edificações e aplicação em uma célula-teste." Sba: Controle & Automação Sociedade Brasileira de Automatica 21, no. 1 (February 2010): 01–13. http://dx.doi.org/10.1590/s0103-17592010000100001.

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Este artigo trata o problema de controle de conforto térmico para ocupantes de edificações. Conforto térmico e um conceito de difícil definição e neste trabalho, utilisa-ze índice PMV (Predicted Mean Vote) para sua avaliação. Através deste índice, duas estratégias de controle preditivo caracterizadas por ter restrições terminais, denominadas aqui de PMV-MBPC (PMV Model Based Predictive Controller), são apresentadas. Na primeira estratégia, a gestão do conforto termico é realizada através da geração de sinais de referêsencia para o controlador, que otimiza o valor de PMV dentro de uma zona térmica da edificação. Na segunda, o modelo de PMV está incluso nos cálculos de previsão do controlador, gerando um modelo não-linear com estrutura Wiener. Resultados relacionados com a garantia de estabilidade do sistema em malha fechada são propostos. Neste contexto, um ambiente para testes (célula-teste) de sistemas de controle é descrita e a primeira abordagem é então implementada em tempo real neste ambiente usando um aquecedor a óleo. Resultados experimentais ilustram o desempenho do sistema controle para conforto térmico. Adicionalmente, resultados de simulação, conduzidos com dados climáticos horários, ilustram também o desempenho dos algoritmos de controle.
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Wang, Yaqi, and Zhigang Liu. "Suppression Research Regarding Low-Frequency Oscillation in the Vehicle-Grid Coupling System Using Model-Based Predictive Current Control." Energies 11, no. 7 (July 10, 2018): 1803. http://dx.doi.org/10.3390/en11071803.

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Recently, low-frequency oscillation (LFO) has occurred many times in high-speed railways and has led to traction blockades. Some of the literature has found that the stability of the vehicle-grid coupling system could be improved by optimizing the control strategy of the traction line-side converter (LSC) to some extent. In this paper, a model-based predictive current control (MBPCC) approach based on continuous control set in the dq reference frame for the traction LSC for electric multiple units (EMUs) is proposed. First, the mathematical predictive model of one traction LSC is deduced by discretizing the state equation on the alternating current (AC) side. Then, the optimal control variables are calculated by solving the performance function, which involves the difference between the predicted and reference value of the current, as well as the variations of the control voltage. Finally, combined with bipolar sinusoidal pulse width modulation (SPWM), the whole control algorithm based on MBPCC is formed. The simulation models of EMUs’ dual traction LSCs are built in MATLAB/SIMULINK to verify the superior dynamic and static performance, by comparing them with traditional transient direct current control (TDCC). A whole dSPACE semi-physical platform is established to demonstrate the feasibility and effectiveness of MBPCC in real applications. In addition, the simulations of multi-EMUs accessed in the vehicle-grid coupling system are carried out to verify the suppressing effect on LFO. Finally, to find the impact of external parameters (the equivalent leakage inductance of vehicle transformer, the distance to the power supply, and load resistance) on MBPCC’s performance, the sensitivity analysis of these parameters is performed. Results indicate that these three parameters have a tiny impact on the proposed method but a significant influence on the performance of TDCC. Both oscillation pattern and oscillation peak under TDCC can be easily influenced when these parameters change.
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Amiryousefi, Ali, Ville Kinnula, and Jing Tang. "Bayes in Wonderland! Predictive Supervised Classification Inference Hits Unpredictability." Mathematics 10, no. 5 (March 5, 2022): 828. http://dx.doi.org/10.3390/math10050828.

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The marginal Bayesian predictive classifiers (mBpc), as opposed to the simultaneous Bayesian predictive classifiers (sBpc), handle each data separately and, hence, tacitly assume the independence of the observations. Due to saturation in learning of generative model parameters, the adverse effect of this false assumption on the accuracy of mBpc tends to wear out in the face of an increasing amount of training data, guaranteeing the convergence of these two classifiers under the de Finetti type of exchangeability. This result, however, is far from trivial for the sequences generated under Partition Exchangeability (PE), where even umpteen amount of training data does not rule out the possibility of an unobserved outcome (Wonderland!). We provide a computational scheme that allows the generation of the sequences under PE. Based on that, with controlled increase of the training data, we show the convergence of the sBpc and mBpc. This underlies the use of simpler yet computationally more efficient marginal classifiers instead of simultaneous. We also provide a parameter estimation of the generative model giving rise to the partition exchangeable sequence as well as a testing paradigm for the equality of this parameter across different samples. The package for Bayesian predictive supervised classifications, parameter estimation and hypothesis testing of the Ewens sampling formula generative model is deposited on CRAN as PEkit package.
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Roset *, Bas, and Henk Nijmeijer. "Observer-based model predictive control." International Journal of Control 77, no. 17 (November 20, 2004): 1452–62. http://dx.doi.org/10.1080/00207170412331326855.

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Vassileva, Svetla, and Štefan Kozák. "NN Model-Based Predictive Control." IFAC Proceedings Volumes 33, no. 13 (June 2000): 495–500. http://dx.doi.org/10.1016/s1474-6670(17)37239-7.

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Roset, Bas, and Henk Nijmeijer. "Observer based model predictive control." IFAC Proceedings Volumes 37, no. 13 (September 2004): 769–74. http://dx.doi.org/10.1016/s1474-6670(17)31318-6.

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Dissertations / Theses on the topic "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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Books on the topic "Model-based predictive control (MBPC)"

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1936-, Clarke D., ed. Advances in model-based predictive control. Oxford: Oxford University Press, 1994.

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Tahirovic, Adnan, and Gianantonio Magnani. Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5049-7.

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Tahirovic, Adnan. Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning. London: Springer London, 2013.

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Rossiter, J. A. Model-Based Predictive Control. Edited by J. A. Rossiter. CRC Press, 2017. http://dx.doi.org/10.1201/9781315272610.

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Rossiter, J. A. Model-Based Predictive Control a Practical Approach. Taylor & Francis Group, 2003.

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Rossiter, J. A. Model-Based Predictive Control: A Practical Approach. Taylor & Francis Group, 2003.

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Rossiter, J. A. Model-Based Predictive Control: A Practical Approach. Taylor & Francis Group, 2017.

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Rossiter, J. A. Model-Based Predictive Control: A Practical Approach. Taylor & Francis Group, 2013.

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Model-Based Predictive Control: A Practical Approach (Crc Press Control Series,). CRC, 2003.

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Multi-parametric model-based control. Weinheim: Wiley-VCH, 2006.

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Book chapters on the topic "Model-based predictive control (MBPC)"

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Klaučo, Martin, and Michal Kvasnica. "Model Predictive Control." In MPC-Based Reference Governors, 15–34. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17405-7_3.

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Camacho, Eduardo F., and Carlos Bordons. "Model Based Predictive Controllers." In Model Predictive Control, 13–31. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3398-8_2.

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Camacho, Eduardo F., and Carlos Bordons. "Introduction to Model Based Predictive Control." In Model Predictive Control, 1–11. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3398-8_1.

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Zhang, Ridong, Anke Xue, and Furong Gao. "Model Predictive Control Based on Extended State Space Model." In Model Predictive Control, 17–27. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_2.

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Zhang, Ridong, Anke Xue, and Furong Gao. "Predictive Functional Control Based on Extended State Space Model." In Model Predictive Control, 29–35. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_3.

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Lann, M. V., M. Cabassud, and G. Casamatta. "Adaptive Model Predictive Control." In Methods of Model Based Process Control, 427–57. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0135-6_17.

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Zhang, Ridong, Anke Xue, and Furong Gao. "Model Predictive Control Based on Extended Non-minimal State Space Model." In Model Predictive Control, 37–50. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_4.

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Zhang, Ridong, Anke Xue, and Furong Gao. "Predictive Functional Control Based on Extended Non-minimal State Space Model." In Model Predictive Control, 51–57. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_5.

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Fernandez-Camacho, Eduardo, and Carlos Bordons-Alba. "Model Based Predictive Controllers." In Model Predictive Control in the Process Industry, 9–37. London: Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3008-6_2.

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Christofides, Panagiotis D., Jinfeng Liu, and David Muñoz de la Peña. "Lyapunov-Based Model Predictive Control." In Networked and Distributed Predictive Control, 13–45. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-582-8_2.

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Conference papers on the topic "Model-based predictive control (MBPC)"

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Zhu, K. Y. "Model-based predictive controller." In International Conference on Control '94. IEE, 1994. http://dx.doi.org/10.1049/cp:19940273.

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Johnstone, A. "MPC model based predictive control." In UKACC Control 2006 Mini Symposia. IEE, 2006. http://dx.doi.org/10.1049/ic:20060262.

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Kocijan, J., R. Murray-Smith, C. E. Rasmussen, and A. Girard. "Gaussian process model based predictive control." In Proceedings of the 2004 American Control Conference. IEEE, 2004. http://dx.doi.org/10.23919/acc.2004.1383790.

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Dhar, Abhishek, and Shubhendu Bhasin. "Tube based Adaptive Model Predictive Control." In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9029450.

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Zhong-Hua Pang and Guo-Ping Liu. "Model-based recursive networked predictive control." In 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2010. http://dx.doi.org/10.1109/icsmc.2010.5642322.

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Capponi, Lino. "Direct Model Predictive Control Using Model Based Design." In 2021 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE). IEEE, 2021. http://dx.doi.org/10.1109/precede51386.2021.9681020.

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Gu, Ruirui, Shuvra S. Bhattacharyya, and Williams S. Levine. "Dataflow-based implementation of model predictive control." In 2009 American Control Conference. IEEE, 2009. http://dx.doi.org/10.1109/acc.2009.5160255.

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Lu, Weiping, and D. Grant Fisher. "Nonminimal Model based Long Range Predictive Control." In 1990 American Control Conference. IEEE, 1990. http://dx.doi.org/10.23919/acc.1990.4791006.

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Riehl, James R., Gaemus E. Collins, and Joao P. Hespanha. "Cooperative graph-based model predictive search." In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4435025.

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Nguyen, Tam W., Syed Aseem Ul Islam, Adam L. Bruce, Ankit Goel, Dennis S. Bernstein, and Ilya V. Kolmanovsky. "Output-Feedback RLS-Based Model Predictive Control*." In 2020 American Control Conference (ACC). IEEE, 2020. http://dx.doi.org/10.23919/acc45564.2020.9148011.

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Reports on the topic "Model-based predictive control (MBPC)"

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Li, Dinggen, and Yang Ye. The Control of Air-Fuel Ratio of the Engine Based on Model Predictive Control. Warrendale, PA: SAE International, October 2012. http://dx.doi.org/10.4271/2012-32-0050.

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Aswani, Anil, Humberto Gonzalez, S. S. Sastry, and Claire Tomlin. Statistical Results on Filtering and Epi-convergence for Learning-Based Model Predictive Control. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada558989.

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An Input Linearized Powertrain Model for the Optimal Control of Hybrid Electric Vehicles. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0741.

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Models of hybrid powertrains are used to establish the best combination of conventional engine power and electric motor power for the current driving situation. The model is characteristic for having two control inputs and one output constraint: the total torque should be equal to the torque requested by the driver. To eliminate the constraint, several alternative formulations are used, considering engine power or motor power or even the ratio between them as a single control input. From this input and the constraint, both power levels can be deduced. There are different popular choices for this one control input. This paper presents a novel model based on an input linearizing transformation. It is demonstrably superior to alternative model forms, in that the core dynamics of the model (battery state of energy) are linear, and the non-linearities of the model are pushed into the inputs and outputs in a Wiener/Hammerstein form. The output non-linearities can be approximated using a quadratic model, which creates a problem in the linear-quadratic framework. This facilitates the direct application of linear control approaches such as LQR control, predictive control, or Model Predictive Control (MPC). The paper demonstrates the approach using the ELectrified Vehicle library for sImulation and Optimization (ELVIO). It is an open-source MATLAB/Simulink library designed for the quick and easy simulation and optimization of different powertrain and drivetrain architectures. It follows a modelling methodology that combines backward-facing and forward-facing signal path, which means that no driver model is required. The results show that the approximated solution provides a performance that is very close to the solution of the original problem except for extreme parts of the operating range (in which case the solution tends to be driven by constraints anyway).
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