Nonlinear Model Predictive Control, Frank Allg?wer; Alex Zheng

Автор: Grune
Название: Nonlinear Model Predictive Control
ISBN: 0857295004 ISBN-13(EAN): 9780857295002
Издательство: Springer
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Цена: 149060.00 T
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Описание: 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.

Автор: Ellis
Название: Economic Model Predictive Control
ISBN: 3319411071 ISBN-13(EAN): 9783319411071
Издательство: Springer
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Цена: 130430.00 T
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Описание: 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.

Автор: Carlos Ocampo-Martinez
Название: Model Predictive Control of Wastewater Systems
ISBN: 1447157184 ISBN-13(EAN): 9781447157182
Издательство: Springer
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Цена: 113180.00 T
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Описание: This book shows how sewage systems can be modeled and controlled within the framework of model predictive control (MPC). A MATLAB(R) toolbox (available for download) will assist readers in implementing the MPC methods described within a sewer network.

Автор: Camacho
Название: Model Predictive Control
ISBN: 1852336943 ISBN-13(EAN): 9781852336943
Издательство: Springer
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Цена: 65210.00 T
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Описание: The second edition of "Model Predictive Control" provides a thorough introduction to theoretical and practical aspects of the most commonly used MPC strategies. It bridges the gap between the powerful but often abstract techniques of control researchers and the more empirical approach of practitioners.

Автор: J.A. Rossiter
Название: Model-Based Predictive Control: A Practical Approach
ISBN: 0849312914 ISBN-13(EAN): 9780849312915
Издательство: Taylor&Francis
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Цена: 83710.00 T
Наличие на складе: Поставка под заказ.
Описание: Analyzes predictive control from its base mathematical foundation. This work introduces basic MPC concepts and demonstrates how they are applied in the design and control of systems, experiments, and industrial processes. It outlines how to model, provide robustness, handle constraints, ensure feasibility, and guarantee stability.

Автор: Lalo Magni; Davide Martino Raimondo; Frank Allg?we
Название: Nonlinear Model Predictive Control
ISBN: 3642010938 ISBN-13(EAN): 9783642010934
Издательство: Springer
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Цена: 148020.00 T
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Описание: 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.

Автор: Luigi Del Re; Frank Allg?wer; Luigi Glielmo; Carlo
Название: Automotive Model Predictive Control
ISBN: 1849960704 ISBN-13(EAN): 9781849960700
Издательство: Springer
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Цена: 139310.00 T
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Описание: The proceedings of the Automotive Model Predictive Control: Models, Methods and Applications workshop investigates whether constrained predictive control is reasonable in automotive control and what is necessary for its application.

Автор: Gergely Tak?cs; Boris Roha?-Ilkiv
Название: Model Predictive Vibration Control
ISBN: 144716072X ISBN-13(EAN): 9781447160724
Издательство: Springer
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Цена: 191550.00 T
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Описание: With more than 170 illustrations, this volume provides vital insights into the predictive control of lightly damped vibrating structures through efficient algorithms capable of low-frequency vibration control, guaranteed stability and constraint feasibility.

Название: Distributed model predictive control made easy
ISBN: 9400770057 ISBN-13(EAN): 9789400770058
Издательство: Springer
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Цена: 156720.00 T
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Описание: Distributed Model Predictive Control Made Easy