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Dissertations / Theses on the topic 'Nonlinear predictive control'

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Published: 4 June 2021

Last updated: 26 July 2025

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1

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

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2

Youssef, Ahmed Medhat Mohamed. "Nonlinear predictive flight control system design." Thesis, University of Strathclyde, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401502.

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3

Siller-Alcalá, Irma Irasema. "Nonlinear continuous-time generalised predictive control." Thesis, University of Glasgow, 1998. http://theses.gla.ac.uk/2090/.

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The development of the nonlinear version of the Continuous-time Generalised Predictive Control (NCGPC) is presented. Unlike the linear version, the nonlinear version is developed in state-space form and shown to include Nonlinear Generalised Minimum Variance (NGMV), and a new algorithm, Nonlinear Predictive Generalised Minimum Variance (NPGMV), as special cases. Through simulations, it is demonstrated that NCGPC can deal with nonlinear systems whose relative degree is not well defined and nonlinear systems with unstable zero dynamics. Geometric approaches, such as exact linearisation, are show

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4

Simminger, Jerome C. "A constrained multivariable nonlinear predictive controller." Thesis, Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/10152.

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5

Savvidis, Petros. "Nonlinear control : an LPV nonlinear predictive generalised minimum variance perspective." Thesis, University of Strathclyde, 2017. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=27947.

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This thesis describes new developments in nonlinear controllers for industrial applications. It first introduces the Nonlinear Generalised Minimum Variance (NGMV) control algorithm, for Linear Parameter Varying systems (LPV). This combines the benefits of the basic NGMV algorithm in dealing with nonlinearities, where a black box input model can be used, and adds an option to also approximate a nonlinear system with an LPV output subsystem. The models can therefore represent LPV systems and characteristics including saturation, discontinuities and time-varying dynamics. The next major contribut

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6

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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<p>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

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7

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 evaluati

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8

Bal, Llanjun. "Nonlinear Predictive Control Based On NARMAX Models." Thesis, University of Sheffield, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522535.

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9

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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10

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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<p>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 th

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11

Peterson, Tod J. "Nonlinear predictive control of a semibatch polymerization reaction." Thesis, Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/10982.

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12

Huang, Gongsheng. "Predictive control of highly uncertain and nonlinear systems." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442848.

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13

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

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14

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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15

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

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16

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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17

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<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 115-124).<br>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 cen

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18

Chen, Yutao. "Algorithms and Applications for Nonlinear Model Predictive Control with Long Prediction Horizon." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3421957.

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Fast implementations of NMPC are important when addressing real-time control of systems exhibiting features like fast dynamics, large dimension, and long prediction horizon, as in such situations the computational burden of the NMPC may limit the achievable control bandwidth. For that purpose, this thesis addresses both algorithms and applications. First, fast NMPC algorithms for controlling continuous-time dynamic systems using a long prediction horizon have been developed. A bridge between linear and nonlinear MPC is built using partial linearizations or sensitivity update. In order to

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19

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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20

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 wi

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21

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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22

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

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23

Herceg, Domagoj. "Stochastic model predictive control of nonlinear and uncertain systems." Thesis, IMT Alti Studi Lucca, 2020. http://e-theses.imtlucca.it/309/1/Herceg_phdthesis.pdf.

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This thesis attempts to shed some additional light on pressing questions regarding the control of uncertain systems. Special focus is given to systems with uncertain uncertainty (inexactly known distribution), numerical optimization methods to enable the use of proposed advanced optimization methods in practice and systems controlled by an economic controller where stability is not always the primary objective. Current state-of-the-art methods often neglect that underlying uncertainty in a stochastic model is, in fact, uncertain as well in the sense that its probability distribution f

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24

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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25

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 probl

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26

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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27

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 sensiti

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28

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 magneti

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29

Desaraju, Vishnu R. "Safe, Efficient, and Robust Predictive Control of Constrained Nonlinear Systems." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/954.

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As autonomous systems are deployed in increasingly complex and uncertain environments, safe, accurate, and robust feedback control techniques are required to ensure reliable operation. Accurate trajectory tracking is essential to complete a variety of tasks, but this may be difficult if the system’s dynamics change online, e.g., due to environmental effects or hardware degradation. As a result, uncertainty mitigation techniques are also necessary to ensure safety and accuracy. This problem is well suited to a receding-horizon optimal control formulation via Nonlinear Model Predictive Control (

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30

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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31

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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<p>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 th

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32

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

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33

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 ate

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34

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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35

Ouyang, Hua. "Networked predictive control systems : control scheme and robust stability." Thesis, University of South Wales, 2007. https://pure.southwales.ac.uk/en/studentthesis/networked-predictive-control-systems(9c6178d7-e6a4-420b-b35f-2d62d35ff5b0).html.

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Networked predictive control is a new research method for Networked Control Systems (NCS), which is able to handle network-induced problems such as time-delay, data dropouts, packets disorders, etc. while stabilizing the closed-loop system. This work is an extension and complement of networked predictive control methodology. There is always present model uncertainties or physical nonlinearity in the process of NCS. Therefore, it makes the study of the robust control of NCS and that of networked nonlinear control system (NNCS) considerably important. This work studied the following three proble

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36

Filippo, Marco. "Stabilizing nonlinear model predictive control in presence of disturbances and off - line approximations of the control law." Doctoral thesis, Università degli studi di Trieste, 2011. http://hdl.handle.net/10077/4519.

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2009/2010<br>One of the more recent and promising approaches to control is the Receding Horizon one. Due to its intrinsic characteristics, this methodology, also know as Model Predictive Control, allows to easily face disturbances and model uncertainties: indeed at each sampling instant the control action is recalculated on the basis of the reached state (closed loop). More in detail, the procedure consists in the minimization of an adequate cost function with respect to a control input sequence; then the first element of the optimal sequence is applied. The whole procedure is then continuousl

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37

Hausberger, Thomas [Verfasser]. "Nonlinear High-Speed Model Predictive Control with Long Prediction Horizons for Power-Converter Systems / Thomas Hausberger." Düren : Shaker, 2021. http://d-nb.info/1233548271/34.

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38

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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<p>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.</p><p>The first part treats control of systems described by nonlinear difference equations, possibly with uncertain ter

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39

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 hi

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40

Knights, Benjamin D. H. "Development of a nonlinear predictive control algorithm and its application to flotation." Master's thesis, University of Cape Town, 2001. http://hdl.handle.net/11427/5305.

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Bibliography: leaves 118-123.<br>This study consists of four clearly defined and interlinked objectives. The first is the development of a method of solving nonlinear optimal control problems. This method is then used to solve the underlying optimal control problems. This method is then used to solve the underlying optimal control problem in a nonliear model predictive control (NMPC) strategy. By way of a case study, and to further understanding of the mechanisms of flotation, a dynamic model of a flotation circuit is developed. This nonlinear dynamic model is then used in the NMPC strategy to

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41

Saraf, Nilay. "Bounded-variable least-squares methods for linear and nonlinear model predictive control." Thesis, IMT Alti Studi Lucca, 2019. http://e-theses.imtlucca.it/296/1/Saraf_phdthesis.pdf.

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This dissertation presents an alternative approach to formulate and solve optimization problems arising in real-time model predictive control (MPC). First it has been shown that by using a quadratic penalty function, the linear MPC optimization problem can be formulated as a least-squares problem subject to bounded variables while directly employing models in their input/output form. A theoretical analysis on stability and optimality is included with a comparison against the conventional condensed approach based on linear state-space models. These concepts are straightforwardly exten

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42

Chellaboina, Vijaya-Sekhar. "Robust stability and performance for linear and nonlinear uncertain systems with structured uncertainty." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/12903.

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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. Compar

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44

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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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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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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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 dynam

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Deng, Jiamei. "Predictive control of nonlinear systems using feedback linearisation based on dynamic neural networks." Thesis, University of Reading, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433463.

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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-dependen

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