Dissertations / Theses: 'Nonlinear Model Predictive Control - NMPC'

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Norén, Christoffer. "Path Planning for Autonomous Heavy Duty Vehicles using Nonlinear Model Predictive Control." Thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-95547.

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In the future autonomous vehicles are expected to navigate independently and manage complex traffic situations. This thesis is one of two theses initiated with the aim of researching which methods could be used within the field of autonomous vehicles. The purpose of this thesis was to investigate how Model Predictive Control could be used in the field of autonomous vehicles. The tasks to generate a safe and economic path, to re-plan to avoid collisions with moving obstacles and to operate the vehicle have been studied. The algorithm created is set up as a hierarchical framework defined by a high and a low level planner. The objective of the high level planner is to generate the global route while the objectives of the low level planner are to operate the vehicle and to re-plan to avoid collisions. Optimal Control problems have been formulated in the high level planner for the use of path planning. Different objectives of the planning have been investigated e.g. the minimization of the traveled length between the start and the end point. Approximations of the static obstacles' forbidden areas have been made with circles. A Quadratic Programming framework has been set up in the low level planner to operate the vehicle to follow the high level pre-computed path and to locally re-plan the route to avoid collisions with moving obstacles. Four different strategies of collision avoidance have been implemented and investigated in a simulation environment.

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Penet, Maxime. "Robust Nonlinear Model Predictive Control based on Constrained Saddle Point Optimization : Stability Analysis and Application to Type 1 Diabetes." Phd thesis, Supélec, 2013. http://tel.archives-ouvertes.fr/tel-00968899.

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This thesis deals with the design of a robust and safe control algorithm to aim at an artificial pancreas. More precisely we will be interested in controlling the stabilizing part of a classical cure. To meet this objective, the design of a robust nonlinear model predictive controller based on the solution of a saddle point optimization problem is considered. Also, to test the controller performances in a realistic case, numerical simulations on a FDA validated testing platform are envisaged.In a first part, we present an extension of the usual nonlinear model predictive controller designed to robustly control, in a sampled-data framework, systems described by nonlinear ordinary differential equations. This controller, which computes the best control input by considering the solution of a constrained saddle point optimization problem, is called saddle point model predictive controller (SPMPC). Using this controller, it is proved that the closed-loop is Ultimately Bounded and, with some assumptions on the problem structure, Input-to State practically Stable. Then, we are interested in numerically solving the corresponding control problem. To do so, we propose an algorithm inspired from the augmented Lagrangian technique and which makes use of adjoint model.In a second part, we consider the application of this controller to the problem of artificial blood glucose control. After a modeling phase, two models are retained. A simple one will be used to design the controller and a complex one will be used to simulate realistic virtual patients. This latter is needed to validate our control approach. In order to compute a good control input, the SPMPC controller needs the full state value. However, the sensors can only provide the value of blood glucose. That is why the design of an adequate observer is envisaged. Then, numerical simulations are performed. The results show the interest of the approach. For all virtual patients, no hypoglycemia event occurs and the time spent in hyperglycemia is too short to induce damageable consequences. Finally, the interest of extending the SPMPC approach to consider the control of time delay systems in a sampled-data framework is numerically explored.

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Kozubík, Michal. "Aplikace nelineárního prediktivního řízení pro pohon se synchronním motorem." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400605.

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This thesis focuses on the possibilities of application of nonlinear model predictive control for electric drives. Specifically, for drives with a permanent magnet synchronous motor. The thesis briefly describes the properties of this type of drive and presents its mathematical model. After that, a nonlinear model of predictive control and methods of nonlinear optimization, which form the basis for the controller output calculation, are described. As it is used in the proposed algorithm, the Active set method is described in more detail. The thesis also includes simulation experiments focusing on the choice of the objective function on the ability to control the drive. The same effect is examined for the different choices of the length of the prediction horizon. The end of the thesis is dedicated to the comparison between the proposed algorithm and commonly used field oriented control. The computational demands of the proposed algorithm are also measured and compared to the used sampling time.

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Murali, madhavan rathai Karthik. "Synthesis and real-time implementation of parameterized NMPC schemes for automotive semi-active suspension systems." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALT052.

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Cette thèse traite de la synthèse et de la mise en œuvre en temps réel (RT) de schémas de contrôle prédictif non linéaire paramétré (pNMPC) pour les systèmes de suspension semi-active des automobiles. Le schéma pNMPC est basé sur une technique d'optimisation par simulation en boîte noire. Le point essentiel de la méthode est de paramétrer finement le profil d'entrée et de simuler le système pour chaque entrée paramétrée et d'obtenir la valeur approximative de l'objectif et de la violation des contraintes pour le problème pNMPC. Avec les résultats obtenus de la simulation, l'entrée admissible (si elle existe) ayant la valeur objective minimale ou, à défaut, la valeur de violation de contrainte la plus faible est sélectionnée et injectée dans le système et ceci est répété indéfiniment à chaque période de décision. La méthode a été validée expérimentalement sur dSPACE MicroAutoBoX II (MABXII) et les résultats montrent de bonnes performances de l'approche proposée. La méthode pNMPC a également été étendue à une méthode pNMPC parallélisée et la méthode proposée a été mise en œuvre pour le contrôle du système de suspension semi-active d'un demi-véhicule. Cette méthode a été mise en œuvre grâce à des unités de traitement graphique (GPU) qui servent de plate-forme modèle pour la mise en œuvre d'algorithmes parallèles par le biais de ses processeurs multi-cœurs. De plus, une version stochastique de la méthode pNMPC parallélisée est proposée sous le nom de schéma pNMPC à Scénario-Stochastique (SS-pNMPC). Cette méthode a été mise en œuvre et testée sur plusieurs cartes NVIDIA embarquées pour valider la faisabilité de la méthode proposée pour le contrôle du système de suspension semi-active d'un demi-véhicule. En général, les schémas pNMPC parallélisés offrent de bonnes performances et se prêtent bien à un large espace de paramétrage en entrée. Enfin, la thèse propose un outil logiciel appelé "pNMPC - A code generation software tool for implementation of derivative free pNMPC scheme for embedded control systems". L'outil logiciel de génération de code (S/W) a été programmé en C/C++ et propose également une interface avec MATLAB/Simulink. Le logiciel de génération de code a été testé pour divers exemples, tant en simulation que sur du matériel embarqué en temps réel (MABXII), et les résultats semblent prometteurs et viables pour la mise en œuvre de la RT pour des applications réelles. L'outil de génération de code S/W comprend également une fonction de génération de code GPU pour une mise en œuvre parallèle. Pour conclure, la thèse a été menée dans le cadre du projet EMPHYSIS et les objectifs du projet s'alignent sur cette thèse et les méthodes pNMPC proposées sont compatibles avec la norme eFMI
This thesis discusses the synthesis and real-time (RT) implementation of parameterized Nonlinear Model Predictive Control (pNMPC) schemes for automotive semi-active suspension systems. The pNMPC scheme uses a black-box simulation-based optimization method. The crux of the method is to finitely parameterize the input profile and simulate the system for each parameterized input and obtain the approximate objective and constraint violation value for the pNMPC problem. With the obtained results from the simulation, the input with minimum objective value or the least constraint violation value is selected and injected into the system and this is repeated in a receding horizon fashion. The method was experimentally validated on dSPACE MicroAutoBoX II (MABXII) and the results display good performance of the proposed approach. The pNMPC method was also augmented to parallelized pNMPC and the proposed method was implemented for control of semi-active suspension system for a half car vehicle. This method was implemented by virtue of Graphic Processing Units (GPUs) which serves as a paragon platform for implementation of parallel algorithms through its multi-core processors. Also, a stochastic version of the parallelized pNMPC method is proposed which is termed as Scenario-Stochastic pNMPC (SS-pNMPC) scheme and the method was implemented and tested on several NVIDIA embedded boards to verify and validate the RT feasibility of the proposed method for control of semi-active suspension system for a half car vehicle. In general, the parallelized pNMPC schemes provide good performance and also, fares well for large input parameterization space. Finally, the thesis proposes a software tool termed “pNMPC – A code generation software tool for implementation of derivative free pNMPC scheme for embedded control systems”. The code generation software (S/W) tool was programmed in C/C++ and also, provides interface to MATLAB/Simulink. The S/W tested for variety of examples both in simulation as well as on RT embedded hardware (MABXII) and the results looks promising and viable for RT implementation for real world applications. The code generation S/W tool also includes GPU code generation feature for parallel implementation. To conclude, the thesis was conducted under the purview of the EMPHYSIS project and the goals of the project align with this thesis and the proposed pNMPC methods are amenable with eFMI standard

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Azevedo, Diego Sousa de. "Otimização do código do sistema de navegação e controle de robôs móveis baseado em NMPC para embarcar em arquiteturas de baixo custo." Universidade Federal da Paraíba, 2015. http://tede.biblioteca.ufpb.br:8080/handle/tede/7853.

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The purpose of this study is to adapt and embed a navigation system and control of mobile robots, based on NMPC, in a low-cost board existent on the market, to provide sufficient com-putational resources so that the robot is able to converge, without losing performance, using the same horizons applied in a Laptop. The obtained results demonstrate the proposed scenario according with the experiments, proving that it is possible to use low cost boards, to a navigation system and control of mobile robots, based on NMPC, using the same predictive and control horizons applied in a Laptop.
A proposta desse trabalho é adaptar e embarcar um sistema de navegação e controle de robôs móveis, baseado em NMPC, em uma placa de baixo custo já existente no mercado, que dispo-nibilize recursos computacionais suficientes para que o Robô seja capaz de convergir, sem perda de desempenho e utilizando os mesmos horizontes aplicados em um Laptop. Os Resulta-dos obtidos demonstram todo o cenário proposto e de acordo com os experimentos realizados, comprovou-se que é possível o uso de placas de baixo custo, para controle de robôs móveis, baseado em NMPC, utilizando os mesmos horizontes de predição e controle aplicados em uma Laptop.

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Furieri, Luca. "Geometric versus Model Predictive Control based guidance algorithms for fixed-wing UAVs in the presence of very strong wind fields." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11872/.

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The recent years have witnessed increased development of small, autonomous fixed-wing Unmanned Aerial Vehicles (UAVs). In order to unlock widespread applicability of these platforms, they need to be capable of operating under a variety of environmental conditions. Due to their small size, low weight, and low speeds, they require the capability of coping with wind speeds that are approaching or even faster than the nominal airspeed. In this thesis, a nonlinear-geometric guidance strategy is presented, addressing this problem. More broadly, a methodology is proposed for the high-level control of non-holonomic unicycle-like vehicles in the presence of strong flowfields (e.g. winds, underwater currents) which may outreach the maximum vehicle speed. The proposed strategy guarantees convergence to a safe and stable vehicle configuration with respect to the flowfield, while preserving some tracking performance with respect to the target path. As an alternative approach, an algorithm based on Model Predictive Control (MPC) is developed, and a comparison between advantages and disadvantages of both approaches is drawn. Evaluations in simulations and a challenging real-world flight experiment in very windy conditions confirm the feasibility of the proposed guidance approach.

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Sriniwas, Ganti Ravi. "Nonlinear model predictive control." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/10267.

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Al, Seyab Rihab Khalid Shakir. "Nonlinear model predictive control using automatic differentiation." Thesis, Cranfield University, 2006. http://hdl.handle.net/1826/1491.

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Although nonlinear model predictive control (NMPC) might be the best choice for a nonlinear plant, it is still not widely used. This is mainly due to the computational burden associated with solving online a set of nonlinear differential equations and a nonlinear dynamic optimization problem in real time. This thesis is concerned with strategies aimed at reducing the computational burden involved in different stages of the NMPC such as optimization problem, state estimation, and nonlinear model identification. A major part of the computational burden comes from function and derivative evaluations required in different parts of the NMPC algorithm. In this work, the problem is tackled using a recently introduced efficient tool, the automatic differentiation (AD). Using the AD tool, a function is evaluated together with all its partial derivative from the code defining the function with machine accuracy. A new NMPC algorithm based on nonlinear least square optimization is proposed. In a first–order method, the sensitivity equations are integrated using a linear formula while the AD tool is applied to get their values accurately. For higher order approximations, more terms of the Taylor expansion are used in the integration for which the AD is effectively used. As a result, the gradient of the cost function against control moves is accurately obtained so that the online nonlinear optimization can be efficiently solved. In many real control cases, the states are not measured and have to be estimated for each instance when a solution of the model equations is needed. A nonlinear extended version of the Kalman filter (EKF) is added to the NMPC algorithm for this purpose. The AD tool is used to calculate the required derivatives in the local linearization step of the filter automatically and accurately. Offset is another problem faced in NMPC. A new nonlinear integration is devised for this case to eliminate the offset from the output response. In this method, an integrated disturbance model is added to the process model input or output to correct the plant/model mismatch. The time response of the controller is also improved as a by–product. The proposed NMPC algorithm has been applied to an evaporation process and a two continuous stirred tank reactor (two–CSTR) process with satisfactory results to cope with large setpoint changes, unmeasured severe disturbances, and process/model mismatches. When the process equations are not known (black–box) or when these are too complicated to be used in the controller, modelling is needed to create an internal model for the controller. In this thesis, a continuous time recurrent neural network (CTRNN) in a state–space form is developed to be used in NMPC context. An efficient training algorithm for the proposed network is developed using AD tool. By automatically generating Taylor coefficients, the algorithm not only solves the differentiation equations of the network but also produces the sensitivity for the training problem. The same approach is also used to solve online the optimization problem of the NMPC. The proposed CTRNN and the predictive controller were tested on an evaporator and two–CSTR case studies. A comparison with other approaches shows that the new algorithm can considerably reduce network training time and improve solution accuracy. For a third case study, the ALSTOM gasifier, a NMPC via linearization algorithm is implemented to control the system. In this work a nonlinear state–space class Wiener model is used to identify the black–box model of the gasifier. A linear model of the plant at zero–load is adopted as a base model for prediction. Then, a feedforward neural network is created as the static gain for a particular output channel, fuel gas pressure, to compensate its strong nonlinear behavior observed in open–loop simulations. By linearizing the neural network at each sampling time, the static nonlinear gain provides certain adaptation to the linear base model. The AD tool is used here to linearize the neural network efficiently. Noticeable performance improvement is observed when compared with pure linear MPC. The controller was able to pass all tests specified in the benchmark problem at all load conditions.

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Fannemel, Åsmund Våge. "Dynamic Positioning by Nonlinear Model Predictive Control." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8921.

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This thesis discusses the theoretical aspects of the unscented Kalman filter (UKF) and nonlinear model predictive control (NMPC) and try to evaluate their practical value in a dynamic positioning (DP) system. A nonlinear horizontal vessel model is used as the basis for performing state, disturbance, and parameter estimation, and attempts at controling the vessel using NMPC are made. It is shown that the extended Kalman filter (EKF), which is much used in various navigation applications including DP, is outperformed both theoretically and practically in simulations by the UKF. Much of which is due to the UKF's improved approximation of the estimated system's true stochastic properties. An attempt to estimate the current from the hydrodynamical damping forces have been applied and shown to be working when the vessel is not subjected to other slowly-varying drift forces. It is implemented a dual estimation approach to try to estimate hydrodynamic damping, which is a very real problem for actual vessels and DP systems. A theoretical evaluation of NMPC is performed and it is concluded that NMPC schemes could fulfill a need in vessel control and DP. Its combination of model based control, optimization approach to achieving performance and predictive properties are indeed useful also for DP. It is found that NMPC could be a step towards a unified control approach combining low and high speed reference tracking, station-keeping and several other control operations which today are handled by separate control approaches. NMPC provides the control designer with an exceptional amount of freedom when quantifying the performance, that it is impossible not to find some use for NMPC.

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Balbis, Luisella. "Nonlinear model predictive control for industrial applications." Thesis, University of Strathclyde, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501892.

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Breyholtz, Øyvind. "Nonlinear Model Predictive Pressure Control during Drilling Operations." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9697.

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Drilling into mature, depleted fields is often difficult because of tight pressure margins. Increasing the pressure control will enable wells that previously were considered undrillable, to be drilled. Enabling drilling and increased oil recovery from depleted fields would most likely lead to a substantial increase in profit margains. A better pressure control will also increase the safety of the drilling crew, because the risk of unwanted situations such as a kick or a blow-out is decreased, also reducing the risk of unwanted environmental influence, e.g. oil spill. To compensate for the lack of a continuous measurement of the bottomhole pressure during drilling operations, an adpative observer of the bottomhole pressure is implemented. The observer implemented is tested, and shows promising results in estimating both the bottomhole pressure and the friction coefficient in the well during a pipe connection procedure. To control the pressure in the well, a low-order nonlinear model predicitve controller is developed, and it has been tested to perform well during the pipe connection procedure, where it maintains the pressure within the predefined boundaries. In this thesis both the obsever and the controller will be tested against an artificial well; simulated by a commercial software.

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Hampson, S. P. "Nonlinear model predictive control of a hydraulic actuator." Thesis, University of Canterbury. Mechanical Engineering, 1995. http://hdl.handle.net/10092/6032.

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The main objective of this thesis is the development and implementation of a nonlinear optimal controller for a hydraulic positioning system. The controller is able to respond rapidly as well as take care of the changing dynamics within the hydraulic system. The necessary attributes for a hydraulic actuator controller are determined by analysing the problems generally associated with hydraulic drives and reviewing the control methods that have been applied in the past. It is concluded that while significant advancements have been made in disturbance rejection, little effort has been placed on the optimal, or minimum time specifications which are frequently demanded by positioning systems. It is also noted that high perfoffi1ance hydraulic drives are prone to cavitation and a controller must necessarily avoid this. The design of a hydraulic test rig is discussed and a novel valve drive circuit that allows direct digital control is presented. The ability of the rig to demonstrate typical control problems is established by experimental testing. The purpose of the test rig is to aid in the modelling process and for controller testing. Power Bond Graphs are used to model the experimental rig and a comparison between a nonlinear model and experimental data shows good correlation. A linear model is also considered and shown to be ineffective at representing the rig dynamics over a range of inputs. By formulating an idealised model, valuable insights into the dynamic characteristics are obtained and the directional dependent gain of single ended rams explained. The performance capabilities of the hydraulic rig are benchmarked by calculating the minimum time response of the hydraulic system subject to constraints on the actuator pressures, load velocity and position. A number of test cases are examined. The research objectives of high performance and flexible constraint handling make model predictive control (MPC) an ideal approach. Model predictive controllers have been successfully applied within the chemical process industry but their application to robotics is hindered by the excessive computational requirements of the algorithm. Furthermore they are typically linear and so in their present form unsuitable. By simplifying the optimisation procedure involved in the MPC algorithm an implementable, nonlinear version of the controller has been tested. The controller is able to constrain the values of pressure, velocity and position within prescribed boundaries, thus eliminating the need for extra hydraulic components. Moreover, the speed of response is comparable to the theoretical optimum. The work reported in this thesis contributes to the field of hydraulic systems control as it presents a novel, nonlinear optimal controller for a hydraulic positioning system. The controller differs from others reported in the literature in that it allows for the plant nonlinearities and forces the system to operate within prescribed boundaries on the state variables. As will be shown this eliminates the need for extra hydraulic components.

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Verschueren, Robin [Verfasser], and Moritz [Akademischer Betreuer] Diehl. "Convex approximation methods for nonlinear model predictive control." Freiburg : Universität, 2018. http://d-nb.info/1192660641/34.

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Lee, Jaehwa. "Linear and nonlinear distributed economic model predictive control." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/23936.

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Model predictive control (MPC), also called receding horizon control, is a control technique to determine control actions for systems by using mathematical optimization theory such as linear or nonlinear programming. It is widely adopted for industrial applications because of its capability of dealing with constraints. For implementation of MPC we solve an on-line optimization problem which minimizes the object function with respect to the given constraints. We commonly adopt convex cost function, which is minimum at the set-point, since by minimizing this cost over horizons we can obtain the convergence of states to the desired set-point. This thesis, however, considers MPC with economically defined objective functions, and implements it in decentralized manner. The key difference of the economic objectives, acquired from the actual value of plants to operate, is that they are not necessarily minimum at the best steady-state, which we decide as the set-point for the state for the operations. Distributed system usually refers to a large-scale system which consists of multiple subsystems interacting with each other. In cooperative MPC which we deal with throughout this thesis, all the subsystems share and optimize the common cost. The main difficulty of this control arises from the coupled inputs and states between subsystems, and the effect between them. While computing consideration on state estimation of other subsystems should be taken into account for the controller design, so large computational burden is unavoidable. We divide the computation into several small problems, and suggest the iterations between the subsystems for the improvement of performance. For linear systems the convex sum of the computation, or estimation, of each subsystem generates the feasible input sequence at any number of iteration. Furthermore, we define and investigate individual feasibility for nonlinear systems. For both cases we prove the iterates converge to the Nash equilibria under some assumptions including asymptotic average constraints. For application example, we investigate the consecutive-competitive reactions, whose resultant substance and byproduct compete to be produced more through the reactions, with numerical simulations.

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Zhu, Yongjie. "Constrained nonlinear model predictive control for vehicle regulation." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1222177849.

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Lopez, Brett Thomas. "Adaptive robust model predictive control for nonlinear systems." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122395.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 115-124).
Modeling error and external disturbances can severely degrade the performance of Model Predictive Control (MPC) in real-world scenarios. Robust MPC (RMPC) addresses this limitation by optimizing over control policies but at the expense of computational complexity. An alternative strategy, known as tube MPC, uses a robust controller (designed offline) to keep the system in an invariant tube centered around a desired nominal trajectory (generated online). While tube MPC regains tractability, there are several theoretical and practical problems that must be solved for it to be used in real-world scenarios. First, the decoupled trajectory and control design is inherently suboptimal, especially for systems with changing objectives or operating conditions. Second, no existing tube MPC framework is able to capture state-dependent uncertainty due to the complexity of calculating invariant tubes, resulting in overly-conservative approximations. And third, the inability to reduce state-dependent uncertainty through online parameter adaptation/estimation leads to systematic error in the trajectory design. This thesis aims to address these limitations by developing a computationally tractable nonlinear tube MPC framework that is applicable to a broad class of nonlinear systems.
"This work was supported by the National Science Foundation Graduate Research Fellowship under Grant No. 1122374, by the DARPA Fast Lightweight Autonomy (FLA) program, by the NASA Convergent Aeronautics Solutions project Design Environment for Novel Vertical Lift Vehicles (DELIVER), and by ARL DCIST under Cooperative Agreement Number W911NF- 17-2-0181"--Page 7.
by Brett T. Lopez.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics

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Lucia, Sergio [Verfasser]. "Robust Multi-stage Nonlinear Model Predictive Control / Sergio Lucia." Aachen : Shaker, 2015. http://d-nb.info/1071527835/34.

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Drca, Ivana. "Nonlinear Model Predictive Control of the Four Tank Process." Thesis, KTH, Reglerteknik, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-106237.

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Model predictive control techniques are widely used in the process industry. They are considered methods that give good performance and are able to operate during long periods without almost any intervention. Model predictive control is also the only technique that is able to consider model restrictions. Almost all industrial processes have nonlinear dynamics, however most MPC applications are based on linear models. Linear models do not always give a sufficiently adequate representation of the system and therefore nonlinear model predictive control techniques have to be considered. Working with nonlinear models give rise to a wide range of difficulties such as, non convex optimization problems, slow processes and a different approach to guarantee stability . This project deals with nonlinear model predictive control and is written at the University of Seville at the department of Systems and Automatic control and at the department of Automatic Control at KTH. The first objective is to control the nonlinear Four Tank Process using nonlinear model predictive control. Objective number two is to investigate if and how the computational time and complexity can be reduced. Simulations show that a nonlinear model predictive control algorithm is developed with satisfactory results. The algorithm is fast enough and all restrictions are respected for initial state values inside of the terminal set as well as for initial state values outside of the terminal set. Feasibility and stability is ensured for both short as well as for longer prediction horizon, guaranteeing that the output reaches the reference. Hence the choice of a short respectively long prediction horizon is a trade off between shorter computational time versus better precision. Regarding the reduction of the computational time, penalty functions have been implemented in the optimization problem converting it to an unconstrained optimization problem including a PHASE-I problem. Results show that this implementation give approximately the same computational time as for the constrained optimization problem. Precision is good for implementations with penalty functions both for long and short prediction horizons and initial state values inside and outside of the terminal set.

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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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Findeisen, Rolf. "Nonlinear model predictive control a sampled data feedback perspective /." [S.l. : s.n.], 2004.

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Quachio, Raphael. "Identificação de sistemas não-lineares de modelos com estrutura de Wiener e Hammerstein para NMPC." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/3/3139/tde-07022019-104111/.

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Esta tese tem por objetivo a obtenção de modelos que apresentem melhor desempenho quando utilizados em controladores preditivos baseados em modelo (Model-based Predictive Control, MPC). Ao longo dos últimos 25 anos diversos trabalhos propuseram métodos baseados na minimização de uma função de predição múltiplos passos à frente, que se caracteriza por ser uma função não linear. Estes métodos foram denominados MPC Relevent Identification (MRI). A maioria destes artigos propõe técnicas para a obtenção de modelos lineares. Ao longo dos últimos 5 anos, alguns métodos, também baseados na minimização da função de predição múltiplos passos à frente, foram propostos para a identificação de modelos não lineares. Estes trabalhos são baseados na minimização direta da função de custo não linear, para obter com estrutura NARMAX (Nonlinear Autoregressive Moving Average with exogenous inputs). Entretanto, estruturas simplificadas de controladores MPC não lineares podem ser obtidas utilizando modelos com estruturas de Wiener e de Hammerstein. Esta tese apresenta novos resultados teóricos que permitem a obtenção de algoritmos de identificação MRI para modelos com estrutura de Wiener e Hammerstein, sem a necessidade de minimizar a função de custo não linear. Além da demonstração dos resultados teóricos, novos algoritmos são propostos tendo a sua capacidade de predição, propriedades estatísticas e aplicação em controladores MPC não lineares avaliadas.
This thesis focuses on obtaining models that may produce a better performance of Model-based Predictive Controllers (MPC). Several papers published in the last 25 years have proposed methods based on the minimization of multi-step ahead prediction functions, which are inherently nonlinear. These methods have been called MPC Relevant Identification (MRI). Most of the papers focused on obtaining linear models. In the last 5 years, some methods have been proposed to obtain nonlinear models based on the minimization of the same cost function. These papers were based on the direct minimization of the nonlinear cost function to produce models with NARMAX (nonlinear Autoregressive Moving Average with exogenous inputs) structure. However, simplified MPC schemes may be obtained using models with Wiener and Hammerstein structures. This thesis presents new theoretical results which allow the development of MRI identification algorithms for models with Wiener and Hammerstein structures, without the need to perform the minimization of the nonlinear cost function. Besides the proof of theoretical results, new algorithms are developed and have their prediction capability statistical properties and performance in nonlinear MPC controllers evaluated.

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Johannessen, Morten Krøtøy, and Torgeir Myrvold. "Stick-Slip Prevention of Drill Strings Using Nonlinear Model Reduction and Nonlinear Model Predictive Control." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9112.

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The main focus of this thesis is aspects in the development of a system for prevention of stick-slip oscillations in drill strings that are used for drilling oil wells. Stick-slip is mainly caused by elasticity of the drill string and changing frictional forces at the bit; static frictional forces are higher than the kinetic frictional forces which make the bit act in a manner where it sticks and then slips, called stick-slip. Stick-slip leads to excessive bit wear, premature tool failures and a poor rate of penetration. A model predictive controller (MPC) should be a suitable remedy for this problem; MPC has gained great success in constrained control problems where tight control is needed. Friction is a highly nonlinear phenomenon and for that reason is it obvious that a nonlinear model is preferred to be used in the MPC to get prime control. Obviously it is of great importance that the internal model used in the MPC is of a certain quality, and as National Oilwell Varco (NOV) has developed a nonlinear drill string model in Simulink, it will be useful to check over this model. This model was therefore verified with a code-to-code comparison and validated using logging data provided from NOV. As the model describing the dynamics of the drill string is somewhat large, a nonlinear model reduction is needed due to the computational complexity of solving a nonlinear model predictive control problem. This nonlinear model reduction is based on the technique of balancing the empirical Gramians, a method that has proven to be successful for a variety of systems. A nonlinear drill string model has been reduced and implemented to a nonlinear model predictive controller (NMPC) and simulated for different scenarios; all proven that NMPC is able to cope with the stick-slip problem. Comparisons have been made with a linear MPC and an existing stick-slip prevention system, SoftSpeed, developed by National Oilwell Varco.

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de, Villiers J. P. "Monte Carlo approaches to nonlinear optimal and model predictive control." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.598462.

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This work explores the novel use of advanced Monte Carlo techniques in the disciplines of nonlinear optimal and model predictive control. The interrelation between the subjects of estimation, random sampling and optimisation is exploited to expand the application of advanced numerical. Bayesian inference techniques to the control setting. Firstly, the deterministic optimal control problem is considered. Sophisticated inter-dimensional population Markov Chain Monte Carlo (MCMC) techniques are proposed to solve the nonlinear optimal control problem. The linear quadratic and Acrobot example problems are used as demonstration of the relevant techniques. Secondly, these methods are extended to the Nonlinear Model Predictive Control (NMPC) setting with uncertain state observations. In this case, two variants of the novel Particle Predictive Controller (PPC) are developed. These PPC algorithms are successfully applied to an F-16 aircraft terrain following problem.

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Felipe, Dominguez Luis Felipe Dominguez. "Advances in multiparametric nonlinear programming & explicit model predictive control." Thesis, Imperial College London, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.536023.

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Yang, Xue. "Advanced-Multi-Step and Economically Oriented Nonlinear Model Predictive Control." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/574.

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This dissertation addresses two issues that arise in the field of Nonlinear Model Predictive Control (NMPC): computational delay and stability of economically oriented NMPC. NMPC has gained wide attention through the application of dynamic optimization. It has the ability to handle variable bounds and multi-input-multi-output systems. However, computational delay caused by large size of nonlinear programming (NLP) problems may lead to deterioration of controller performance and system stability. In this thesis we propose an advanced-multi-step formulation of NMPC (amsNMPC) based on NLP sensitivity. The basic idea of amsNMPC is to solve a background NLP problem in advance to get predictions of future manipulated variables. These are then updated online using NLP sensitivity when the actual states are obtained. This method could be applied to optimization problems whose solutions require multiple sampling times. We then analyze the nominal and robust stabilities of the two approaches. Two examples are studied to evaluate the performance of amsNMPC. The ultimate goal of any operation strategy for a process plant is to make profit. Traditionally this goal could be achieved by a two-layer Real-time Optimization (RTO) system, where the upper layer solves a steady state problem aiming at optimizing economic performance to get the optimal setpoints for the controlled variables in the layer below. The lower layer then keeps the controlled variables at their given setpoints using MPC/NMPC. However, there are some problems with this two-layer structure. One of the solutions is to combine these two layers and include the economic criterion directly into the cost function of the lower layer controller when an optimization-based controller such as MPC is used. This approach is often referred to as Economic MPC. The issue with Economic NMPC is that the controller may not be stable. In this dissertation we analyze its Lyapunov stability property and propose to stabilize it by adding quadratic regularization terms to the objective function, and we also provide a method to calculate the most appropriate weights on regularization terms to ensure the stability of Economic NMPC while achieving the best possible economic performance. Several challenging case studies are used to demonstrate these concepts.

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Moeti, Sekhonyana. "Formal analysis of state estimation for nonlinear model predictive control." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/20065.

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The main goal of this study is to carry out a closed-loop performance analysis of state estimation methods when implemented in the formulation of nonlinear model predictive control. The analysis is facilitated by two nonlinear optimal state estimation methods: augmented state EKF (ASEKF) and augmented state UKF (ASUKF) for comparison purposes. Each state estimation method is coupled to the same NMPC controller to form state estimation-based NMPC controllers, that is, to form the ASEKF-NMPC and ASUKFNMPC controllers. The resulting NMPC controllers are applied for position control of the magnetic levitation system to validate their closed-loop performances. The ASEKFNMPC and ASUKF-NMPC controllers are further applied for the angular position control of the inverted pendulum mounted on a cart system for comparative analysis. The controlled system is perturbed with different error sources: output step disturbance and 5%parametric plant-model mismatch. Output step disturbance is introduced to the system to disturb the pendulum from its upright position while the 5% mismatch is applied to the parameters of the model of the controlled system throughout the simulation. To facilitate fair analysis, Pareto front ranking method is chosen as an evaluation method whereby the cost functions are defined according to the author's preferences. The cost functions served as performance markers for analyzing performance of ASEKF and ASUKF in NMPC formulation in multidimensional space. The tunable parameters of the ASEKFNMPC and ASUKF-NMPC controllers are chosen to be the decision variables of the evaluation problem. The state estimation methods are evaluated in terms of estimation accuracy, system's response time, peak overshoot and control performance. The Level Diagrams tool is used for good visualization of the Pareto fronts to evaluate which estimator performs better in the closed-loop. Finally, the points on the Level Diagrams which provide a performance closest to the desired are selected and tested through simulation runs on the inverted pendulum on a moving cart system.

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Agarwal, Naveen. "Nonlinear model predictive control of a semi-batch emulsion polymerization reactor." Thesis, Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/8456.

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Dahl, Becedas Martin. "Linear and Nonlinear Model Predictive Control of a Wave Energy Converter." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284252.

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The topic of this thesis has been to design a regulator that maximizes the energytransfer from kinetic wave energy to electrical energy. The wave energyconverter used, is of the heaving point absorber type, which is developed bythe company CorPower Ocean AB. Two models have been used, a linear modeland a model in which a selection of nonlinear forces have been active. A Linearand Nonlinear Model Predictive Controller have been developed, usingMatlab’s optimizer fmincon. The cost function of these controllers has alsobeen varied in an attempt to take the infinite horizon into account, using aterminal weight. In order to get an idea of the performance, these controllerswere compared with a differently formulated Model Predictive Controller thatused Matlab’s optimizer quadprog. The latter generally performed betterwith respect to extracted energy. It seemed as the choice of optimizer couldhave an impact on the results. When nonlinear drag and net restoring stiffnessforces were introduced to the model, the Nonlinear Model Predictive Controllerincreased the output energy compared to its linear counterpart.
Detta examensarbete har syftat till att utveckla en regulator som maximerarenergiöverföringen från kinetisk vågenergi till elektrisk energi. Vågkraftverketsom använts är av typen punktabsorberande system, som utvecklas av företagetCorPower Ocean AB. Två modeller har använts, en linjär approximationsamt en modell där ett urval av olinjära krafter varit aktiva. En Linjär samtOlinjär Modellprediktiv Regulator har utvecklats, där Matlabs optimeringslösarefmincon användes. Kostnadsfunktionen för dessa regulatorer har ävenvarierats i ett försök att ta hänsyn till oändliga tidshorisonten. För att få enuppfattning av prestandan jämfördes dessa regulatorer med en annorlunda formuleradModellprediktiv Regulator som använde Matlabs optimeringslösarequadprog. Den sistnämnda presterade generellt bättre med avseende på utvunnenenergi. Regulatorerna som togs fram i detta arbete betedde sig interikigt som väntat då de i flera fall minskade i energi med en längre tidshorisont.Att försöka beskriva oändliga horisonten i kostnadsfunktionen resulteradeoftast i något mer utvunnen energi, det skulle dock krävas vidare arbeteför att kunna dra slutsatser kring denna metod. Det fanns indikationer på attfmincon presterade sämre med längre horisont, även här skulle det krävasmer arbete för att kunna fastställa huruvida detta var orsaken eller inte.

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Dunn, John. "An investigation into neural network assisted model predictive control for nonlinear systems." Thesis, Brunel University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367442.

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La, Huu Chuong [Verfasser], and Hans Georg [Akademischer Betreuer] Bock. "Dual Control for Nonlinear Model Predictive Control / Huu Chuong La ; Betreuer: Hans Georg Bock." Heidelberg : Universitätsbibliothek Heidelberg, 2016. http://d-nb.info/1180616316/34.

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Rahideh, Akbar. "Model identification and robust nonlinear model predictive control of a twin rotor MIMO system." Thesis, Queen Mary, University of London, 2009. http://qmro.qmul.ac.uk/xmlui/handle/123456789/1885.

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This thesis presents an investigation into a number of model predictive control (MPC) paradigms for a nonlinear aerodynamics test rig, a twin rotor multi-input multi-output system (TRMS). To this end, the nonlinear dynamic model of the system is developed using various modelling techniques. A comprehensive study is made to compare these models and to select the best one to be used for control design purpose. On the basis of the selected model, a state-feedback multistep Newton-type MPC is developed and its stability is addressed using a terminal equality constraint approach. Moreover, the state-feedback control approach is combined with a nonlinear state observer to form an output-feedback MPC. Finally, a robust MPC technique is employed to address the uncertainties of the system. In the modelling stage, analytical models are developed by extracting the physical equations of the system using the Newtonian and Lagrangian approaches. In the case of the black-box modelling, artificial neural networks (ANNs) are utilised to model the TRMS. Finally, the grey-box model is used to enhance the performance of the white-box model developed earlier through the optimisation of parameters using a genetic algorithm (GA) based approach. Stability analysis of the autonomous TRMS is carried out before designing any control paradigms for the system. In the control design stage, an MPC method is proposed for constrained nonlinear systems, which is the improvement of the multistep Newton-type control strategy. The stability of the proposed state-feedback MPC is guaranteed using terminal equality constraints. Moreover, the formerly proposed MPC algorithm is combined with an unscented Kalman filter (UKF) to formulate an output-feedback MPC. An extended Kalman filter (EKF) based on a state-dependent model is also introduced, whose performance is found to be better compared to that of the UKF. Finally, a robust MPC is introduced and implemented on the TRMS based on a polytopic uncertainty that is cast into linear matrix inequalities (LMI).

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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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Norstedt, Erik, and Olof Bräne. "Model Predictive Climate Control for Electric Vehicles." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446435.

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This thesis explores the possibility of using an optimal control scheme called Model Predictive Control (MPC), to control climatization systems for electric vehicles. Some components of electric vehicles, for example the batteries and power electronics, are sensitive to temperature and for this reason it is important that their temperature is well regulated. Furthermore, like all vehicles, the cab also needs to be heated and cooled. One of the weaknesses of electric vehicles is their range, for this reason it is important that the temperature control is energy efficient. Once the range of electric vehicles is increased the down sides compared to traditional combustion engine vehicles decrease, which could lead to an increase in the usage of electric vehicles. This could in turn lead to a decrease of greenhouse gas emission in the transportation sector. With the help of MPC it is possible for the controller to take more factors into consideration when controlling the system than just temperature and in this thesis the power consumption and noise are also taken into consideration. A simple model where parts of the climate system’s circuits were seen as point masses was developed, with nonlinear heat transfers occurring between them, which in turn were controlled by actuators such as fans, pumps and valves. The model was created using Simulink and MATLAB, and the MPC toolbox was used to develop nonlinear MPC controllers to control the climate system. A standard nonlinear MPC, a nonlinear MPC with custom cost functions and a PI controller where all developed and compared in simulations of a cooling scenario. The controllers were designed to control the temperatures of the battery, power electronics and the cab of an electric vehicle. The results of the thesis indicate that MPC could reduce power consumption for the climate control system, it was however not possible to draw any final conclusions as the PI controller that the MPC controllers were compared to was not well optimized for the system. The MPC controllers could benefit from further work, most importantly by applying a more sophisticated tuning method to the controller weights. What was certain was that it is possible to apply this type of centralized controller to very complex systems and achieve robustness without external logic. Even with the controller keeping track of six different temperatures and controlling 15 actuators, the control loop runs much faster than real time on a modern computer which shows promise with regard to implementing it on an embedded system.

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Koppauer, Herwig [Verfasser]. "Nonlinear model predictive control of an automotive waste heat recovery system / Herwig Koppauer." Düren : Shaker, 2019. http://d-nb.info/1196486247/34.

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Huang, Rui. "Nonlinear Model Predictive Control and Dynamic Real Time Optimization for Large-scale Processes." Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/29.

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This dissertation addresses some of the theoretical and practical issues in optimized operationsin the process industry. The current state-of-art is to decompose the optimizationinto the so-called two-layered structure, including real time optimization (RTO) and advancedcontrol. Due to model discrepancy and inconsistent time scales in different layers,this structure may render suboptimal solutions. Therefore, the dynamic real time optimization(D-RTO) or economically-oriented nonlinear model predictive control (NMPC)that directly optimizes the economic performance based on first-principle dynamic modelsof processes has become an emerging technology. However, the integration of the firstprincipledynamic models is likely to introduce large scale optimization problems, whichneed to be solved online. The associated computational delay may be cumbersome for theonline applications.We first derive a first-principle dynamic model for an industrial air separation unit (ASU).The recently developed advanced step method is used to solve both set-point tracking andeconomically-oriented NMPC online. It shows that set-point tracking NMPC based on thefirst-principle model has superior performance against that with linear data-driven model.In addition, the economically-oriented NMPC generates around 6% cost reduction comparedto set-point tracking NMPC. Moreover the advanced step method reduces the onlinecomputational delay by two orders of magnitude.Then we deal with a realistic set-point tracking control scenario that requires achievingoffset-free behavior in the presence of plant-model mismatch. Moreover, a state estimatoris used to reconstruct the plant states from outputs. We propose two formulations usingNMPC and moving horizon estimation (MHE) and we show both approaches are offsetfreeat steady state. Moreover, the analysis can be extended to NMPC coupled with othernonlinear observers. This strategy is implemented on the ASU process.After that, we study the robust stability of output-feedback NMPC in the presence of plantmodelmismatch. The Extended Kalman Filter (EKF), which is a widely-used technologyin industry is chosen as the state estimator. First we analyze the stability of the estimationerror and a separation-principle-like result indicates that the stability result is the same asthe closed-loop case. We further study the impact of this estimation error on the robuststability of the NMPC.Finally, nominal stability is analyzed for the D-RTO, i.e. economically-oriented NMPC,for cyclic processes. Moreover, two economically-oriented NMPC formulations with guaranteednominal stability are proposed. They ensure the system converges to the optimalcyclic steady state.

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Abokhatwa, Salah G. "Distributed nonlinear state-dependent model predictive control and estimation for power generation plants." Thesis, University of Strathclyde, 2014. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=23207.

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Centralized model predictive control (MPC) is often considered impractical, inflexible and unsuitable for controlling large-scale systems due to several factors such as large computational effort and difficulty to meet all operational objectives. Therefore, industrial large-scale systems are usually controlled by a distributed control framework. In this thesis, novel sequential nonlinear Distributed Model Predictive Control (DMPC) algorithms for large-scale systems that can handle constraints are proposed. The proposed algorithms are based on nonlinear MPC strategy, which uses a state-dependent nonlinear model to reduce the complexity of solving optimization problem. In this distributed framework, the overall system is divided into several interconnected subsystems and each subsystem is controlled by local MPC. These local MPCs solve convex optimization problem and exchange information via one directional communication channel at each sampling time to achieve the global performance. The proposed algorithms are applied to an industrial power plant model to improve power generation efficiency. A non-linear dynamic model of Combined Cycle Power Plant (CCPP) using the laws of physics was first developed and simulated using decentralized PID controllers. Then, a supervisory controller using linear constrained MPC was designed to tune the performance of the PID controllers. Next, a supervisory centralized nonlinear model predictive control (NMPC) algorithm based on state-dependent models was developed to control the nonlinear plant over a wide operating range. Finally, two sequential DMPC algorithms based on state-dependent models were developed. The lack of states measurement were handled by designing nonlinear distributed state estimation algorithms using state-dependent differential Riccati equation (SDDRE) Kalman filter. Numerical simulation results show that the performance of the proposed DMPC algorithms is close to the centralized NMPC but computationally more efficient compared to the centralized one.

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Lu, Yaohui. "Scheduling quasi-min-max model predictve control." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/11692.

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Braghieri, Giovanni. "Application of robust nonlinear model predictive control to simulating the control behaviour of a racing driver." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/275524.

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The work undertaken in this research aims to develop a mathematical model which can replicate the behaviour of a racing driver controlling a vehicle at its handling limit. Most of the models proposed in the literature assume a perfect driver. A formulation taking human limitations into account would serve as a design and simulation tool for the automotive sector. A nonlinear vehicle model with five degrees of freedom under the action of external disturbances controlled by a Linear Quadratic Regulator (LQR) is first proposed to assess the validity of state variances as stability metrics. Comparison to existing stability and controllability criteria indicates that this novel metric can provide meaningful insights into vehicle performance. The LQR however, fails to stabilise the vehicle as tyres saturate. The formulation is extended to improve its robustness. Full nonlinear optimisation with direct transcription is used to derive a controller that can stabilise a vehicle at the handling limit under the action of disturbances. The careful choice of discretisation method and track description allow for reduced computing times. The performance of the controller is assessed using two vehicle configurations, Understeered and Oversteered, in scenarios characterised by increasing levels of non- linearity and geometrical complexity. All tests confirm that vehicles can be stabilised at the handling limit. Parameter studies are also carried out to reveal key aspects of the driving strategy. The driver model is validated against Driver In The Loop simulations for simple and complex manoeuvres. The analysis of experimental data led to the proposal of a novel driving strategy. Driver randomness is modelled as an external disturbance in the driver Neuromuscular System. The statistics of states and controls are found to be in good agreement. The prediction capabilities of the controller can be considered satisfactory.

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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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Coetzee, Lodewicus Charl. "Robust nonlinear model predictive control of a closed run-of-mine ore milling circuit." Thesis, Pretoria : [s.n.], 2009. http://upetd.up.ac.za/thesis/available/etd-09272009-103725/.

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Martinsen, Frode. "The Optimization Algorithm rFSQP with Application to Nonlinear Model Predictive Control of Grate Sintering." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2001. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-78.

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This thesis contributes to the research on optimization algorithms for nonlinear programming, and to the application of such algorithms to nonlinear model predictive control.

Regarding the contribution to research on algorithms for nonlinear programming, a novel algorithm is put forward with a complete theory for global and local convergence. This is the main contribution of the thesis. The algorithm, named rFSQP, is a reduced Hessian Feasible Sequential Quadratic Programming method. It remains feasible with respect to nonlinear inequalities at all SQP iterations, but nonlinear equality constraints are treated as in general reduced Hessian SQP methods. The rFSQP algorithm is implemented in MATLAB and tested on a number of small scale problems with encouraging results. However, the algorithm is designed for large scale problems with few degrees of freedom. Some preliminary testing of the algorithm on large scale problems are investigated.

The thesis also contributes to the understanding of the relation between sequential and simultaneous reduced gradient methods, and to the understanding of the relation between discretization methods for dynamical systems and the choice of optimization algorithms.

The thesis also contributes to model based control approaches of grate sintering. Grate sintering is a complex metallurgical process, where melting of solids and fast gas dynamics give rise to stiff process models, i.e. the "time constants" of the system differ by many decades in magnitude. Hence, application of real-time optimization methods like nonlinear model predictive control to the grate sintering process is challenging. The thesis gives a framework for implementing nonlinear model based control of grate sintering by giving a control objective, a nonlinear model and choosing an appropriate discretization scheme. The thesis gives a reduced order model which is less computationally demanding. Data from industrial experiments are used to adapt the model and to assess the control objective.

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Kudruss, Manuel [Verfasser], and Katja [Akademischer Betreuer] Mombaur. "Nonlinear Model Predictive Control for Motion Generation of Humanoids / Manuel Kudruss ; Betreuer: Katja Mombaur." Heidelberg : Universitätsbibliothek Heidelberg, 2020. http://d-nb.info/1210647745/34.

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Bürger, Adrian [Verfasser], and Moritz [Akademischer Betreuer] Diehl. "Nonlinear mixed-integer model predictive control of renewable energy systems : : methods, software, and experiments." Freiburg : Universität, 2020. http://d-nb.info/1225682150/34.

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Park, Junho. "Nonlinear Model Predictive Control for a Managed Pressure Drilling with High-Fidelity Drilling Simulators." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/6792.

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The world's energy demand has been rapidly increasing and is projected to continue growing for at least the next two decades. With increasing global energy demand and competition from renewable energy, the oil and gas industry is striving for more efficient petroleum production. Many technical breakthroughs have enabled the drilling industry to expand the exploration to more difficult drilling such as deepwater drilling and multilateral directional drilling. For example, managed pressure drilling (MPD) offers ceaseless operation with multiple manipulated variables (MV) and wired drill pipe (WDP) provides two-way, high-speed measurements from bottom hole and along-string sensors. These technologies have maximum benefit when applied in an automation system or as a real-time advisory tool. The objective of this study is to investigate the benefit of nonlinear model-based control and estimation algorithms with various types of models. This work presents a new simplified flow model (SFM) for bottomhole pressure (BHP) regulation in MPD operations. The SFM is embedded into model-based control and estimation algorithms that use model predictive control (MPC) and moving horizon estimation (MHE), respectively. This work also presents a new Hammerstein-Wiener nonlinear model predictive controller for BHP regulation. Hammerstein-Wiener models employ input and output static nonlinear blocks before and after linear dynamics blocks to simplify the controller design. The control performance of the new Hammerstein-Wiener nonlinear controller is superior to conventional PID controllers in a variety of drilling scenarios. Conventional controllers show severe limitations in MPD because of the interconnected multivariable and nonlinear nature of drilling operations. BHP control performance is evaluated in scenarios such as drilling, pipe connection, kick attenuation, and mud density displacement and the efficacy of the SFM and Hammerstein-Wiener models is tested in various control schemes applicable to both WDP and mud pulse systems. Trusted high-fidelity drilling simulators are used to simulate well conditions and are used to evaluate the performance of the controllers using the SFM and Hammerstein-Wiener models. The comparison between non-WDP (semi-closed loop) and WDP (full-closed loop) applications validates the accuracy of the SFM under the set of conditions tested and confirms comparability with model-based control and estimation algorithms. The SFM MPC maintains the BHP within ± 1 bar of the setpoint for each investigated scenario, including for pipe connection and mud density displacement procedures that experience a wider operation range than normal drilling.

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Bolin, Tobias. "Nonlinear Approximative Explicit Model Predictive Control Through Neural Networks : Characterizing Architectures and Training Behavior." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264994.

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Model predictive control (MPC) is a paradigm within automatic control notable for its ability to handle constraints. This ability come at the cost of high computational demand, which until recently has limited use of MPC to slow systems. Recent advances have however enabled MPC to be used in embedded applications, where its ability to handle constraints can be leveraged to reduce wear, increase efficiency and improve overall performance in everything from cars to wind turbines. MPC controllers can be made even faster by precomputing the resulting policy and storing it in a lookup table. A method known as explicit MPC. An alternative way of leveraging precomputation is to train a neural network to approximate the policy. This is an attractive proposal both due to neural networks ability to imitate policies for nonlinear systems, and results that indicate that neural networks can efficiently represent explicit MPC policies. Limited work has been done in this area. How the networks are setup and trained therefore tends to reflect recent trends in other application areas rather than being based on what is known to work well for approximating MPC policies. This thesis attempts to alleviate this situation by evaluating how some common neural network architectures and training methods performs when used for this purpose. The evaluations are carried out through a literature study and by training several networks with different architectures to replicate the policy of a nonlinear MPC controller tasked with stabilizing an inverted pendulum. The results suggest that ReLU activation functions give better performance than hyperbolic tangent and SELU functions; and that dropout and batch normalization degrades the ability to approximate policies; and that depth significantly increases the performance. However, the neural network controllers do occasionally exhibit problematic behaviors, such as steady state errors and oscillating control signals close to constraints.
Modell-prediktiv reglering (MPC, efter engelskans Model Predictive Control) är ett paradigm inom reglertekniken som på ett effektivt sätt kan hantera begränsningar i systemet som ska regleras. Den här egenskapen kommer på bekostnad av att MPC kräver mycket datorkraft. Tidigare har användning av den här typen av kontroller därför varit begränsad till långsamma system. På senare tid har framsteg inom hård- och mjukvara dock möjliggjort användning av MPC på inbyggda system. Där kan dess förmåga att hantera begränsningar användas för att minska slitage, öka effektivitet och förbättra prestanda inom allt från bilar till vindkraftverk. Ett sätt att minska beräkningsbördan ytterligare är att beräkna MPC-policyn i förväg och spara den i en tabell. Det här tillvägagångssättet kallas explicit MPC. Ett alternativt tillvägagångssätt är att träna ett neuralt nätverk till att approximera policyn. Potentiellt har det här fördelarna att ett neuralt nätverk inte är begränsat till att efterlikna policys för system med linjär dynamik, och att det finns resultat som pekar på att neurala nätverk är väl lämpade för att lagra policys för explicit MPC. En begränsad mängd arbete har gjorts inom det här området. Hur nätverken designas och tränas tenderar därför att reflektera trender inom andra applikationsområden för neurala nätverk istället för att baseras på vad som fungerar för att implementera MPC. Det här examensarbetet försöker avhjälpa det här problemet. Dels genom en litteraturstudie och dels genom att undersöka hur olika arkitekturer för neurala nätverk beter sig när de tränas för att efterlikna en ickelinjär MPC-kontroller som ska stabilisera en inverterad pendel. Resultaten tyder på att nätverk med ReLU-aktivering ger bättre prestanda än motsvarande nätverk som använder SELU eller tangens hyperbolicus som aktiveringsfunktion. Resultaten visar också att batch noralization och dropout försämmrar nätverkens förmåga att lära sig policyn och att prestandan blir bättre om antalet lager i nätverket ökar. De neurala nätverken uppvisar dock i vissa fall kvalitativa problem, så som statiska fel och oscillerande kontrollsignaler nära begränsningar.

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Kittisupakorn, Paisan. "The use of nonlinear model predictive control techniques for the control of a reactor with exothermic reactions." Thesis, Imperial College London, 1996. http://hdl.handle.net/10044/1/8742.

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Nielsen, Isak. "Modeling and Control of Friction Stir Welding in 5 cm thick Copper Canisters." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-78748.

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Friction stir welding has become a popular forging technique used in many applications. The Swedish Nuclear Fuel and Waste Management Company (SKB) evaluates this method to seal the 5 cm thick copper canisters that will contain the spent nuclear fuel. To produce repetitive, high quality welds, the process must be controlled, and today a cascade controller is used to keep the desired stir zone temperature. In this thesis, the control system is extended to also include a plunge depth controller. Two different approaches are evaluated; the first attempt is a decentralized solution where the cascaded temperature controller is kept, and the second approach uses a non-linear model predictive controller for both depth and temperature. Suitable models have been derived and used to design the controllers; a simpler model for the decentralized control and a more extensive, full model used in the non-linear model predictive controller that relates all the important process variables. The two controller designs are compared according to important performance measures, and the achieved increase in performance with the more complex non-linear model predictive controller is evaluated. The non-linear model predictive controller has not been implemented on the real process. Hence, simulations of the closed loop systems using the full model have been used to compare and evaluate the control strategies. The decentralized controller has been implemented on the real system. Two welds have been made using plunge depth control with excellent experimental results, confirming that the decentralized controller design proposed in this thesis can be successfully used. Even though the controller manages to regulate the plunge depth with satisfying performance, simulations indicate that the non-linear model predictive controller achieves even better closed loop performance. This controller manages to compensate for the cross-connections between the process variables, and the resulting closed loop system is almost decoupled. Further research will reveal which control design that will finally be used.
''Friction stir welding'' har blivit en populär svetsmetod inom många olika tillämpningar. På Svensk Kärnbränslehantering AB (SKB) undersöks möjligheten att använda metoden för att försegla de 5 cm tjocka kopparkapslarna som kommer innehålla det använda kärnbränslet. För att kunna producera repeterbara svetsar utav hög kvalité krävs det att processen regleras. Idag löses detta med en temperaturregulator som reglerar svetszonens temperatur. I detta examensarbete utökas styrsystemet med en regulator för svetsdjupet. Två olika lösningar har utvärderats; först en decentraliserad lösning där temperatur-regulatorn behålls och sedan en lösning med en olinjär modellprediktiv reglering (MPC) som reglerar både djup och temperatur. Passande modeller har tagits fram och har använts för att designa regulatorerna; en enklare modell för den decentraliserade regulatorn och en utökad, komplett modell som används i den olinjära MPC:n och som beskriver alla viktiga variabler i processen. Viktiga prestandamått har jämförts för de båda regulatorstrukturerna och även prestandaökningen med den olinjära MPC:n har utvärderats. Då denna regulator inte har implementerats på den verkliga processen har simuleringar av den kompletta modellen använts för att jämföra och utvärdera regulatorstrukturerna. Den decentraliserade regulatorn har implementerats och testats på processen. Två svetsar har gjorts och de har givit utmärkta resultat, vilket visar att regulatorstrukturen som presenteras i rapporten fungerar bra för reglering av svetsdjupet. Trots att den implementerade regulatorn klarar av att reglera svetsdjupet med godkänt resultat, så visar simuleringar att den olinjära MPC:n ger ännu bättre reglerprestanda. Denna regulator kompenserar för korskopplingar i systemet och resulterar i ett slutet system som är nästan helt frikopplat. Ytterligare forskning kommer avgöra vilken av strategierna som kommer att användas i slutprodukten.

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Delport, Ruanne. "Process identification using second order Volterra models for nonlinear model predictive control design of flotation circuits." Diss., Pretoria : [s.n.], 2004. http://upetd.up.ac.za/thesis/available/etd-05112005-091046.

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Imsland, Lars. "Topics in nonlinear control. : Output Feedback Stabilization and Control of Positive Systems." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-355.

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The contributions of this thesis are in the area of control of systems with nonlinear dynamics. The thesis is divided into three parts. The two first parts are similar in the sense that they both consider output feedback of rather general classes of nonlinear systems, and both approaches are based on mathematical programming (although in quite different ways). The third part contains a state feedback approach for a specific system class, and is more application oriented.

The first part treats control of systems described by nonlinear difference equations, possibly with uncertain terms. The system dynamics are represented by piecewise affine difference inclusions, and for this system class, piecewise affine controller structures are suggested. Controller synthesis inequalities for such controller structures are given in the form of Bilinear Matrix Inequalities (BMIs). A solver for the BMIs is developed. The main contribution is to the output feedback case, where an observer-based controller structure is proposed. The theory is exemplified through two examples.

In the second part the output feedback problem is examined in the setting of Nonlinear Model Predictive Control (NMPC). The state space formulation of NMPC is inherently a state feedback approach, since the state is needed as initial condition for the prediction in the controller. Consequently, for output feedback it is natural to use observers to obtain estimates of the state. A high gain observer is applied for this purpose. It is shown that for several existing NMPC schemes, the state feedback stability properties ``semiglobally'' hold in the output feedback case. The theory is illuminated with a simple example.

Finally, a state feedback controller for a class of positive systems is proposed. Convergence of the state to a certain subset of the first orthant, corresponding to a constant ``total mass'' (interpreting states as masses) is obtained. Conditions are given under which convergence to this set implies asymptotic stability of an equilibrium. Simple examples illustrate some properties of the controller. Furthermore, the control strategy is applied to the stabilization of a gas-lifted oil well, and simulations on a rigorous multi-phase dynamic simulator of such a well demonstrate the controller performance.

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Kufoalor, Dzordzoenyenye Kwame. "Reconfigurable Autopilot Design using Nonlinear Model Predictive Control : Application to High Performance and Autonomous Aircraft." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18439.

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The work presented in this thesis examines several aspects of Nonlinear ModelPredictive Control (NMPC) that display and confirm its promising potentials as apowerful reconfigurable control scheme. The effects of significant nonlinearities andthe intrinsically unstable nature of high performance fighter aircraft, among otherchallenges, have been shown to be well handled in the NMPC framework. Thiswork illustrates how complex control and stability augmentation measures (whichare normally realized through ad hoc mode switching strategies) can be formulatedand implemented as NMPC objectives and constraints. Further suggestions onrobustness strategies for model/plant mismatch and compensation for couplingeffects which are not properly accounted for, have been presented and examined inthis work. Results on fault tolerance of NMPC are also presented and discussed inthis thesis. In this direction, NMPC has been shown to have unique inherent faultdetection capabilities due to its effective utilization of feedback and its internalmodel predictions. Different types of actuator/control surface failures, includingextreme cases of total actuator failure are examined as test cases for the NMPCreconfigurable fault tolerant control scheme developed in this work. The NMPCautopilots are designed for an F-16 fighter aircraft, and the implementation andsimulations were done using ACADO nonlinear optimization solver, interfaced withthe MATLAB/Simulink environment.

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Dissertations / Theses on the topic 'Nonlinear Model Predictive Control - NMPC'

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