Nonlinear Model Predictive Control, AllgÃ¶wer Frank, Zheng Alex

Àâòîð: Camacho Íàçâàíèå: Model Predictive Control ISBN: 1852336943 ISBN-13(EAN): 9781852336943 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 6116 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: 14959 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: Grancharova Íàçâàíèå: Explicit Nonlinear Model Predictive Control ISBN: 3642287794 ISBN-13(EAN): 9783642287794 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 10485 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå: Nonlinear Model Predictive Control (NMPC) has become the accepted methodology to solve complex control problems related to process industries. The main motivation behind explicit NMPC is that an explicit state feedback law avoids the need for executing a numerical optimization algorithm in real time. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation and the possibility to design embedded control systems with low software and hardware complexity.This book considers the multi-parametric Nonlinear Programming (mp-NLP) approaches to explicit approximate NMPC of constrained nonlinear systems, developed by the authors, as well as their applications to various NMPC problem formulations and several case studies. The following types of nonlinear systems are considered, resulting in different NMPC problem formulations:? Nonlinear systems described by first-principles models and nonlinear systems described by black-box models;- Nonlinear systems with continuous control inputs and nonlinear systems with quantized control inputs;- Nonlinear systems without uncertainty and nonlinear systems with uncertainties (polyhedral description of uncertainty and stochastic description of uncertainty);- Nonlinear systems, consisting of interconnected nonlinear sub-systems.The proposed mp-NLP approaches are illustrated with applications to several case studies, which are taken from diverse areas such as automotive mechatronics, compressor control, combustion plant control, reactor control, pH maintaining system control, cart and spring system control, and diving computers.

Àâòîð: Findeisen Rolf, AllgÃ¶wer Frank, Biegler Lorenz Íàçâàíèå: Assessment and Future Directions of Nonlinear Model Predictive Control ISBN: 3540726985 ISBN-13(EAN): 9783540726982 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 14024 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå: 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.

Àâòîð: Lalo Magni; Davide Martino Raimondo; Frank Allg?we Íàçâàíèå: Nonlinear Model Predictive Control ISBN: 3642010938 ISBN-13(EAN): 9783642010934 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 14854 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: 10284 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: Ocampo-Martinez Íàçâàíèå: Model Predictive Control of Wastewater Systems ISBN: 1849963525 ISBN-13(EAN): 9781849963527 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 13980 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå: 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.

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.

Îïèñàíèå: Real-time model predictive controller (MPC) implementation in active vibration control (AVC) is often rendered difficult by fast sampling speeds and extensive actuator-deformation asymmetry. If the control of lightly damped mechanical structures is assumed, the region of attraction containing the set of allowable initial conditions requires a large prediction horizon, making the already computationally demanding on-line process even more complex. Model Predictive Vibration Control provides insight into the predictive control of lightly damped vibrating structures by exploring computationally efficient algorithms which are capable of low frequency vibration control with guaranteed stability and constraint feasibility. In addition to a theoretical primer on active vibration damping and model predictive control, Model Predictive Vibration Control provides a guide through the necessary steps in understanding the founding ideas of predictive control applied in AVC such as:· the implementation of computationally efficient algorithms· control strategies in simulation and experiment and· typical hardware requirements for piezoceramics actuated smart structures. The use of a simple laboratory model and inclusion of over 170 illustrations provides readers with clear and methodical explanations, making Model Predictive Vibration Control the ideal support material for graduates, researchers and industrial practitioners with an interest in efficient predictive control to be utilized in active vibration attenuation.

Àâòîð: Carlos Ocampo-Martinez Íàçâàíèå: Model Predictive Control of Wastewater Systems ISBN: 1447157184 ISBN-13(EAN): 9781447157182 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 11358 ð. Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå: 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.

Îïèñàíèå: In this book, experienced researchers gave a thorough explanation of distributed model predictive control (DMPC): its basic concepts, technologies, and implementation in plant?“wide systems. Known for its error tolerance, high flexibility, and good dynam

Àâòîð: Ellis Íàçâàíèå: Economic Model Predictive Control ISBN: 3319411071 ISBN-13(EAN): 9783319411071 Èçäàòåëüñòâî: Springer Ðåéòèíã: Öåíà: 12233 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.