nonlinear model predictive control theory and algorithms communications and control engineering

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Nonlinear Model Predictive Control

Author : Lars Grüne
ISBN : 9783319460246
Genre : Technology & Engineering
File Size : 42. 2 MB
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This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. The second edition has been substantially rewritten, edited and updated to reflect the significant advances that have been made since the publication of its predecessor, including: • a new chapter on economic NMPC relaxing the assumption that the running cost penalizes the distance to a pre-defined equilibrium; • a new chapter on distributed NMPC discussing methods which facilitate the control of large-scale systems by splitting up the optimization into smaller subproblems; • an extended discussion of stability and performance using approximate updates rather than full optimization; • replacement of the pivotal sufficient condition for stability without stabilizing terminal conditions with a weaker alternative and inclusion of an alternative and much simpler proof in the analysis; and • further variations and extensions in response to suggestions from readers of the first edition. Though primarily aimed at academic researchers and practitioners working in control and optimization, the text is self-contained, featuring background material on infinite-horizon optimal control and Lyapunov stability theory that also makes it accessible for graduate students in control engineering and applied mathematics.

Economic Model Predictive Control

Author : Matthew Ellis
ISBN : 9783319411088
Genre : Technology & Engineering
File Size : 68. 92 MB
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This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency. Specifically, the book proposes: Lyapunov-based EMPC methods for nonlinear systems; two-tier EMPC architectures that are highly computationally efficient; and EMPC schemes handling explicitly uncertainty, time-varying cost functions, time-delays and multiple-time-scale dynamics. The proposed methods employ a variety of tools ranging from nonlinear systems analysis, through Lyapunov-based control techniques to nonlinear dynamic optimization. The applicability and performance of the proposed methods are demonstrated through a number of chemical process examples. The book presents state-of-the-art methods for the design of economic model predictive control systems for chemical processes.In addition to being mathematically rigorous, these methods accommodate key practical issues, for example, direct optimization of process economics, time-varying economic cost functions and computational efficiency. Numerous comments and remarks providing fundamental understanding of the merging of process economics and feedback control into a single framework are included. A control engineer can easily tailor the many detailed examples of industrial relevance given within the text to a specific application. The authors present a rich collection of new research topics and references to significant recent work making Economic Model Predictive Control an important source of information and inspiration for academics and graduate students researching the area and for process engineers interested in applying its ideas.

Modelling And Control Of Dynamic Systems Using Gaussian Process Models

Author : Juš Kocijan
ISBN : 9783319210216
Genre : Technology & Engineering
File Size : 29. 56 MB
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This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control. The book is illustrated by extensive use of examples, line drawings, and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from successful applications including: a gas–liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration. A MATLAB® toolbox, for identification and simulation of dynamic GP models is provided for download.

Handbook Of Model Predictive Control

Author : Saša V. Raković
ISBN : 9783319774893
Genre : Science
File Size : 23. 84 MB
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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.

Modellbasierte Pr Diktive Regelung

Author : Rainer Dittmar
ISBN : 9783486594911
Genre : Technology & Engineering
File Size : 53. 93 MB
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Das Buch bietet eine Einführung in die modellbasierte prädiktive Regelungen einschließlich ihrer Anwendungen in der industriellen Prozessautomatisierung. Ausgewählte Anwendungsbeispiele zeigen dem Leser die Möglichkeiten und den Nutzen dieser Technologie auf.

Advanced Process Control

Author : Rainer Dittmar
ISBN : 9783110497236
Genre : Technology & Engineering
File Size : 25. 52 MB
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Advanced Process Control spielt in der Prozessführung eine große Rolle für den wirtschaftlichen Betrieb verfahrenstechnischer Produktionsanlagen. Neben der Optimierung von PID-Basisregelungen und dem Regelgüte-Management werden Fragen der Modellbildung, vermaschte Regelungsstrukturen, die Entwicklung von Softsensoren zur fortlaufenden Berechnung schwer messbarer Qualitätskenngrößen und modellbasierte prädiktive Mehrgrößenregelungen behandelt.

Robuste Regelung

Author : Jürgen Ackermann
ISBN : 9783662097779
Genre : Technology & Engineering
File Size : 20. 70 MB
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Robuste Regelung stellt einen für die praktische Umsetzung wichtigen Aspekt der Regelungstheorie dar. Sie gibt Auskunft, ob die Einschwingvorgänge linearer Regelsysteme rasch abklingen. Dies ist wichtig bei realen Systemen, bei denen sich starke Änderungen der Betriebsbedingungen einstellen, in der Praxis z.B. bei einem Kran mit variabler Seillänge oder Lastmasse, aber auch bei einem Flugzeug, das mit verschiedenen Geschwindigkeiten in verschiedenen Höhen fliegt. Robuste Regelung von Jürgen Ackermann liefert den neuesten Stand der Verfahren zur Robustheitsanalyse. Es werden Entwurfswerkzeuge (Parameterraum-Verfahren, Gütevektor-Optimierung) vorgestellt und auf die Regelung praktischer mechanischer Systeme aus Automobil- und Luftfahrttechnik angewendet. Angesprochen sind in erster Linie Ingenieure der Elektrotechnik und des Maschinenbaus.

Parametric Optimization Singularities Pathfollowing And Jumps

Author : J. Guddat
ISBN : 9783663121602
Genre : Technology & Engineering
File Size : 59. 93 MB
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Distributed Model Predictive Control Made Easy

Author : José M. Maestre
ISBN : 9789400770065
Genre : Technology & Engineering
File Size : 63. 8 MB
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The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems. This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those who want to gain a deeper insight in the wide range of distributed MPC techniques available.

Identification And Control Using Volterra Models

Author : F.J.III Doyle
ISBN : 9781447101079
Genre : Technology & Engineering
File Size : 24. 4 MB
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This book covers recent results in the analysis, identification and control of systems described by Volterra models. Topics covered include: qualitative behavior of finite Volterra models compared and contrasted with other nonlinear model classes, structural restrictions and extensions to Volterra model class, least squares and stochastic identification approaches, model inversion issues, and direct synthesis and model predictive control design, guidelines for practical applications. Examples are drawn from Chemical, Biological and Electrical Engineering. The book is suitable as a text for a graduate control course, or as a reference for both research and practice.

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