Assessment and Future Directions of Nonlinear Model Predictive Control, Findeisen Rolf, AllgÃ¶wer Frank, Biegler Lorenz

Àâòîð: Camacho Íàçâàíèå: Model Predictive Control ISBN: 1852336943 ISBN-13(EAN): 9781852336943 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 7314 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå: 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.

Àâòîð: Grune Íàçâàíèå: Nonlinear Model Predictive Control ISBN: 0857295004 ISBN-13(EAN): 9780857295002 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 16719 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå: 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.

Àâòîð: Espinosa Jairo, Vandewalle Joos, Wertz Vincent Íàçâàíèå: Fuzzy Logic, Identification and Predictive Control ISBN: 1852338288 ISBN-13(EAN): 9781852338282 Èçäàòåëüñòâî: Springer Öåíà: 17578 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå: 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.

Îïèñàíèå: Predictive Simulation of Semiconductor Processing enables researchers and developers to extend the scaling range of semiconductor devices beyond the parameter range of empirical research. It requires a thorough understanding of the basic mechanisms employed in device fabrication, such as diffusion, ion implantation, epitaxy, defect formation and annealing, and contamination. This book presents an in-depth discussion of our current understanding of key processes and identifies areas that require further work in order to achieve the goal of a comprehensive, predictive process simulation tool.

Àâòîð: Lalo Magni; Davide Martino Raimondo; Frank Allg?we Íàçâàíèå: Nonlinear Model Predictive Control ISBN: 3642010938 ISBN-13(EAN): 9783642010934 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 16602 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå: 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.

Àâòîð: Lars Gr?ne; J?rgen Pannek Íàçâàíèå: Nonlinear Model Predictive Control ISBN: 3319460234 ISBN-13(EAN): 9783319460239 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 11494 ð. Íàëè÷èå íà ñêëàäå: Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå:

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.

Àâòîð: Ellis Íàçâàíèå: Economic Model Predictive Control ISBN: 3319411071 ISBN-13(EAN): 9783319411071 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 13672 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå: 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.

Îïèñàíèå: 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.

Àâòîð: Christofides Íàçâàíèå: Networked and Distributed Predictive Control ISBN: 0857295810 ISBN-13(EAN): 9780857295811 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 14649 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå: 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 book’s methods in greater depth.

Îïèñàíèå: 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.

Àâòîð: Jacques Richalet; Donal O`Donovan Íàçâàíèå: Predictive Functional Control ISBN: 1848824920 ISBN-13(EAN): 9781848824928 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 15625 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå: 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.

Àâòîð: Bari Anasse, Chaouchi Mohamed, Jung Tommy Íàçâàíèå: Predictive Analytics for Dummies ISBN: 1119267005 ISBN-13(EAN): 9781119267003 Èçäàòåëüñòâî: Wiley Ðåéòèíã: Öåíà: 2629 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå: Use Big Data and technology to uncover real-world insights You don`t need a time machine to predict the future. All it takes is a little knowledge and know-how, and Predictive Analytics For Dummies gets you there fast.