Accelerating The Calculation Of Model Predictive Control Laws For Constrained Linear Systems
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Accelerating The Calculation Of Model Predictive Control Laws For Constrained Linear Systems
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Author : Michael Jost
language : en
Publisher:
Release Date : 2015
Accelerating The Calculation Of Model Predictive Control Laws For Constrained Linear Systems written by Michael Jost and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Accelerating The Calculation Of Model Predictive Control Laws For Constrained Linear Systems
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Author :
language : en
Publisher:
Release Date : 2015
Accelerating The Calculation Of Model Predictive Control Laws For Constrained Linear Systems written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Computation In Constrained Stochastic Model Predictive Control Of Linear Systems
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Author : Minyong Shin
language : en
Publisher: Stanford University
Release Date : 2011
Computation In Constrained Stochastic Model Predictive Control Of Linear Systems written by Minyong Shin and has been published by Stanford University this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Despite its sub-optimality, Model Predictive Control (MPC) has received much attention over the recent decades due to its ability to handle constraints. In particular, stochastic MPC, which includes uncertainty in the system dynamics, is one of the most active recent research topics in MPC. In this dissertation, we focus on (1) increasing computation speed of constrained stochastic MPC of linear systems with additive noise and, (2) improving the accuracy of an approximate solution involving systems with additive and multiplicative noise. Constrained MPC for linear systems with additive noise has been successfully formulated as a semidefinite programming problem (SDP) using the Youla parameterization or innovation feedback and linear matrix inequalities. Unfortunately, this method can be prohibitively slow even for problems with moderate size state. Thus, in this thesis we develop an interior point algorithm which can more efficiently solve the problem. This algorithm converts the stochastic problem into a deterministic one using the mean and the covariance matrix as the system state and using affine feedback. A line search interior point method is then directly applied to the nonlinear deterministic optimization problem. In the process, we take advantage of a recursive structure that appears when a control problem is solved via the line search interior point method in order to decrease the algorithmic complexity of the solution. We compare the computation time and complexity of our algorithm against an SDP solver. The second part of the dissertation deals with systems with additive and multiplicative noise under probabilistic constraints. This class of systems differs from the additive noise case in that the probability distribution of a state is neither Gaussian nor known in closed form. This causes a problem when the probability constraints are dealt with. In previous studies, this problem has been tackled by approximating the state as a Gaussian random variable or by approximating the probability bound as an ellipsoid. In this dissertation, we use the Cornish-Fisher expansion to approximate the probability bounds of the constraints. Since the Cornish-Fisher expansion utilizes quantile values with the first several moments, the probabilistic constraints have the same form as those in the additive noise case when the constraints are converted to deterministic ones. This makes the procedure smooth when we apply the developed algorithm to a linear system with multiplicative noise. Moreover, we can easily extend the application of the algorithm to a linear system with additive plus multiplicative noise.
New Directions On Model Predictive Control
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Author : Jinfeng Liu
language : en
Publisher: MDPI
Release Date : 2019-01-16
New Directions On Model Predictive Control written by Jinfeng Liu and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-16 with Engineering (General). Civil engineering (General) categories.
This book is a printed edition of the Special Issue "New Directions on Model Predictive Control" that was published in Mathematics
Predictive Control For Linear And Hybrid Systems
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Author : Francesco Borrelli
language : en
Publisher: Cambridge University Press
Release Date : 2017-06-22
Predictive Control For Linear And Hybrid Systems written by Francesco Borrelli and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-22 with Mathematics categories.
With a simple approach that includes real-time applications and algorithms, this book covers the theory of model predictive control (MPC).
Handbook Of Model Predictive Control
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Author : Saša V. Raković
language : en
Publisher: Springer
Release Date : 2018-09-01
Handbook Of Model Predictive Control written by Saša V. Raković and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-01 with Science categories.
Recent developments in model-predictive control promise remarkable opportunities for designing multi-input, multi-output control systems and improving the control of single-input, single-output systems. This volume provides a definitive survey of the latest model-predictive control methods available to engineers and scientists today. The initial set of chapters present various methods for managing uncertainty in systems, including stochastic model-predictive control. With the advent of affordable and fast computation, control engineers now need to think about using “computationally intensive controls,” so the second part of this book addresses the solution of optimization problems in “real” time for model-predictive control. The theory and applications of control theory often influence each other, so the last section of Handbook of Model Predictive Control rounds out the book with representative applications to automobiles, healthcare, robotics, and finance. The chapters in this volume will be useful to working engineers, scientists, and mathematicians, as well as students and faculty interested in the progression of control theory. Future developments in MPC will no doubt build from concepts demonstrated in this book and anyone with an interest in MPC will find fruitful information and suggestions for additional reading.
Model Predictive Control
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Author : Ridong Zhang
language : en
Publisher: Springer
Release Date : 2018-08-14
Model Predictive Control written by Ridong Zhang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-14 with Technology & Engineering categories.
This monograph introduces the authors’ work on model predictive control system design using extended state space and extended non-minimal state space approaches. It systematically describes model predictive control design for chemical processes, including the basic control algorithms, the extension to predictive functional control, constrained control, closed-loop system analysis, model predictive control optimization-based PID control, genetic algorithm optimization-based model predictive control, and industrial applications. Providing important insights, useful methods and practical algorithms that can be used in chemical process control and optimization, it offers a valuable resource for researchers, scientists and engineers in the field of process system engineering and control engineering.
Recent Advances In Model Predictive Control
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Author : Timm Faulwasser
language : en
Publisher: Springer Nature
Release Date : 2021-04-17
Recent Advances In Model Predictive Control written by Timm Faulwasser and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-17 with Science categories.
This book focuses on distributed and economic Model Predictive Control (MPC) with applications in different fields. MPC is one of the most successful advanced control methodologies due to the simplicity of the basic idea (measure the current state, predict and optimize the future behavior of the plant to determine an input signal, and repeat this procedure ad infinitum) and its capability to deal with constrained nonlinear multi-input multi-output systems. While the basic idea is simple, the rigorous analysis of the MPC closed loop can be quite involved. Here, distributed means that either the computation is distributed to meet real-time requirements for (very) large-scale systems or that distributed agents act autonomously while being coupled via the constraints and/or the control objective. In the latter case, communication is necessary to maintain feasibility or to recover system-wide optimal performance. The term economic refers to general control tasks and, thus, goes beyond the typically predominant control objective of set-point stabilization. Here, recently developed concepts like (strict) dissipativity of optimal control problems or turnpike properties play a crucial role. The book collects research and survey articles on recent ideas and it provides perspectives on current trends in nonlinear model predictive control. Indeed, the book is the outcome of a series of six workshops funded by the German Research Foundation (DFG) involving early-stage career scientists from different countries and from leading European industry stakeholders.
Model Predictive Control
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Author : Basil Kouvaritakis
language : en
Publisher: Springer
Release Date : 2015-12-01
Model Predictive Control written by Basil Kouvaritakis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-01 with Technology & Engineering categories.
For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplicative and stochastic model uncertainty. The book provides: extensive use of illustrative examples; sample problems; and discussion of novel control applications such as resource allocation for sustainable development and turbine-blade control for maximized power capture with simultaneously reduced risk of turbulence-induced damage. Graduate students pursuing courses in model predictive control or more generally in advanced or process control and senior undergraduates in need of a specialized treatment will find Model Predictive Control an invaluable guide to the state of the art in this important subject. For the instructor it provides an authoritative resource for the construction of courses.
Model Based Predictive Control
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Author : J.A. Rossiter
language : en
Publisher: CRC Press
Release Date : 2017-07-12
Model Based Predictive Control written by J.A. Rossiter and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-12 with Technology & Engineering categories.
Model Predictive Control (MPC) has become a widely used methodology across all engineering disciplines, yet there are few books which study this approach. Until now, no book has addressed in detail all key issues in the field including apriori stability and robust stability results. Engineers and MPC researchers now have a volume that provides a complete overview of the theory and practice of MPC as it relates to process and control engineering. Model-Based Predictive Control, A Practical Approach, analyzes predictive control from its base mathematical foundation, but delivers the subject matter in a readable, intuitive style. The author writes in layman's terms, avoiding jargon and using a style that relies upon personal insight into practical applications. This detailed introduction to predictive control introduces basic MPC concepts and demonstrates how they are applied in the design and control of systems, experiments, and industrial processes. The text outlines how to model, provide robustness, handle constraints, ensure feasibility, and guarantee stability. It also details options in regard to algorithms, models, and complexity vs. performance issues.