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Assessment and Future Directions of Nonlinear Model Predictive Control, Findeisen Rolf, Allgöwer Frank, Biegler Lorenz


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: Findeisen Rolf, Allgöwer Frank, Biegler Lorenz
 Assessment and Future Directions of Nonlinear Model Predictive Control
: Springer
:


ISBN: 3540726985
ISBN-13(EAN): 9783540726982
ISBN: 3-540-72698-5
ISBN-13(EAN): 978-3-540-72698-2
/: Paperback
: 642
: 0.997 .
: 2007
: Lecture Notes in Control and Information Sciences
: ENG
: 2007 ed.
: Xii, 644 p.
: 23.57 x 18.31 x 2.39 cm
: Professional & vocational
: Link
:
:
: Thepastthree decadeshaveseenrapiddevelopmentin the areaofmodelpred- tive control with respect to both theoretical and application aspects. This is one of the reasons why nonlinear model predictive control (NMPC) has - joyed signi?cant attention over the past years,with a number of recent advances on both the theoretical and application frontier.
: : 235x155
: Engineers, researchers, and students in control engineering
: NMPC
Obstacle Avoidance
Path Planning
: eng
: Foundations and History of NMPC.- Theoretical Aspects of NMPC.- Numerical Aspects of NMPC.- Robustness, Robust Design, and Uncertainty.- State Estimation and Output Feedback.- Industrial Perspective on NMPC.- NMPC and Process Control.- NMPC for Fast Systems.- Novel Applications of NMPC.- Distributed NMPC, Obstacle Avoidance, and Path Planning.





Nonlinear Model Predictive Control

: Grune
: Nonlinear Model Predictive Control
ISBN: 0857295004 ISBN-13(EAN): 9780857295002
: Springer
:
: 14959 .
  : .

: Nonlinear Model Predictive Control is a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. 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. NMPC schemes with and without stabilizing terminal constraints are detailed and intuitive examples illustrate the performance of different NMPC variants. An introduction to nonlinear optimal control algorithms gives insight into how the nonlinear optimisation routine the core of any NMPC controller works. An appendix covering NMPC software and accompanying software in MATLAB and C++(downloadable from www.springer.com/ISBN) enables readers to perform computer experiments exploring the possibilities and limitations of NMPC.

Model Predictive Control

: Camacho
: Model Predictive Control
ISBN: 1852336943 ISBN-13(EAN): 9781852336943
: Springer
:
: 6116 .
  : .

: Offers an introduction to theoretical and practical aspects of the various MPC strategies. This book attempts to bridge the gap between the techniques of control researchers and the empirical approach of practitioners. It features material on several subjects including commercial MPC schemes. It is intended for students and researchers.

Nonlinear Model Predictive Control

: Lalo Magni; Davide Martino Raimondo; Frank Allg?we
: Nonlinear Model Predictive Control
ISBN: 3642010938 ISBN-13(EAN): 9783642010934
: Springer
:
: 14854 .
  : .

: Over the years significant progress has been achieved in the field of nonlinear model predictive control (NMPC), also referred to as receding horizon control or moving horizon control. This book assesses the status of the NMPC field and discusses future directions and needs.

Nonlinear Model Predictive Control

: Lars Gr?ne; J?rgen Pannek
: Nonlinear Model Predictive Control
ISBN: 3319460234 ISBN-13(EAN): 9783319460239
: Springer
:
: 10284 .
  : .

:

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 routinethe core of any nonlinear model predictive controllerworks. 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

: Ellis
: Economic Model Predictive Control
ISBN: 3319411071 ISBN-13(EAN): 9783319411071
: Springer
:
: 12233 .
  : .

: 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.

Networked and Distributed Predictive Control

: Christofides
: Networked and Distributed Predictive Control
ISBN: 0857295810 ISBN-13(EAN): 9780857295811
: Springer
:
: 13107 .
  : .

: Networked and Distributed Predictive Control presents rigorous, yet practical, methods for the design of networked and distributed predictive control systems the first book to do so. The design of model predictive control systems using Lyapunov-based techniques accounting for the influence of asynchronous and delayed measurements is followed by a treatment of networked control architecture development. This shows how networked control can augment dedicated control systems in a natural way and takes advantage of additional, potentially asynchronous and delayed measurements to maintain closed loop stability and significantly to improve closed-loop performance. The text then shifts focus to the design of distributed predictive control systems that cooperate efficiently in computing optimal manipulated input trajectories that achieve desired stability, performance and robustness specifications but spend a fraction of the time required by centralized control systems. Key features of this book include: new techniques for networked and distributed control system design; insight into issues associated with networked and distributed predictive control and their solution; detailed appraisal of industrial relevance using computer simulation of nonlinear chemical process networks and wind- and solar-energy-generation systems; and integrated exposition of novel research topics and rich resource of references to significant recent work. A full understanding of Networked and Distributed Predictive Control requires a basic knowledge of differential equations, linear and nonlinear control theory and optimization methods and the book is intended for academic researchers and graduate students studying control and for process control engineers. The constant attention to practical matters associated with implementation of the theory discussed will help each of these groups understand the application of the books methods in greater depth.

Model Predictive Control System Design and Implementation Using MATLAB

: Liuping Wang
: Model Predictive Control System Design and Implementation Using MATLAB
ISBN: 1848823304 ISBN-13(EAN): 9781848823303
: Springer
:
: 13980 .
  : .

: Model Predictive Control (MPC) is unusual in receiving on-going interest in both industrial and academic circles. This title proposes methods for design and implementation of MPC systems using basis functions that confer the advantages, including continuous- and discrete-time MPC problems solved in similar design frameworks.

Dynamic Modeling, Predictive Control and Performance Monitoring

: Huang
: Dynamic Modeling, Predictive Control and Performance Monitoring
ISBN: 1848002327 ISBN-13(EAN): 9781848002326
: Springer
:
: 11359 .
  : .

: Covers subjects including closed-loop subspace identification; predictive control design; and, multivariate control performance assessment. This book uses the intermediate subspace matrices, which are obtained directly from the process data and otherwise identified as a first step in the subspace identification methods, directly for the designs.

Predictive Functional Control

: Jacques Richalet; Donal O`Donovan
: Predictive Functional Control
ISBN: 1848824920 ISBN-13(EAN): 9781848824928
: Springer
:
: 13980 .
  : .

: The predictive functional control (PFC) technique was first used to develop a model-based predictive controller that was easy to understand, implement and tune from an instrumentation engineer`s perspective. This book offers the reader with a fundamental understanding of the principles associated with PFC.

Fuzzy Logic, Identification and Predictive Control

: Espinosa Jairo, Vandewalle Joos, Wertz Vincent
: Fuzzy Logic, Identification and Predictive Control
ISBN: 1852338288 ISBN-13(EAN): 9781852338282
: Springer
: 15728 .
  : .

: The complexity and sensitivity of modern industrial processes and systems increasingly require adaptable advanced control protocols. These controllers have to be able to deal with circumstances demanding "judgement" rather than simple "yes/no", "on/off" responses, circumstances where an imprecise linguistic description is often more relevant than a cut-and-dried numerical one. The ability of fuzzy systems to handle numeric and linguistic information within a single framework renders them efficacious in this form of expert control system.Divided into two parts, Fuzzy Logic, Identification and Predictive Control first shows you how to construct static and dynamic fuzzy models using the numerical data from a variety of real-world industrial systems and simulations. The second part demonstrates the exploitation of such models to design control systems employing techniques like data mining.Fuzzy Logic, Identification and Predictive Control is a comprehensive introduction to the use of fuzzy methods in many different control paradigms encompassing robust, model-based, PID-like and predictive control. This combination of fuzzy control theory and industrial serviceability will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of the fuzzy control area for the graduate student.Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Predictive Analytics for Dummies

: Bari Anasse
: Predictive Analytics for Dummies
ISBN: 1118728963 ISBN-13(EAN): 9781118728963
: Wiley
: 2298 .
  : .

Predictive Technology Model for Robust Nanoelectronic Design

: Cao
: Predictive Technology Model for Robust Nanoelectronic Design
ISBN: 1461404444 ISBN-13(EAN): 9781461404446
: Springer
:
: 12233 .
  : .

: Predictive Technology Model for Robust Nanoelectronic Design explains many of the technical mysteries behind the Predictive Technology Model (PTM) that has been adopted worldwide in explorative design research. Through physical derivation and technology extrapolation, PTM is the de-factor device model used in electronic design. This work explains the systematic model development and provides a guide to robust design practice in the presence of variability and reliability issues. Having interacted with multiple leading semiconductor companies and university research teams, the author brings a state-of-the-art perspective on technology scaling to this work and shares insights gained in the practices of device modeling.


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