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ISBN:3764362979
Author: Frank Allgöwer,Alex Zheng
ISBN13: 978-3764362973
Title: Nonlinear Model Predictive Control (Progress in Systems and Control Theory)
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Language: English
Category: Hardware and DIY
Publisher: Birkhäuser; 2000 edition (April 26, 2000)
Pages: 472

Nonlinear Model Predictive Control (Progress in Systems and Control Theory) by Frank Allgöwer,Alex Zheng



There have been many significant advances in this area over the past years, one of the most important ones being its extension to nonlinear systems. This book gives an up-to-date assessment of the current state of the art in the new field of nonlinear model predictive control (NMPC)  .

Nonlinear Model Predictive Control is presently viewed as one of the most promising areas in automatic control. Stability and Robustness of Nonlinear Receding Horizon Control G. De Nicolao, L. Magni and R. Scattolini Abstract. The main design strategies for ensuring stability and robustness of nonlinear RH (Receding-Horizon) control systems are critically surveyed.

Frank Allgöwer, Alex Zheng. inear Model Predictive Control. html?hl ru&id WGr0BwAAQBAJ. There have been many significant advances in this area over the past years, one of the most important ones being its extension to nonlinear systems  . Nonlinear Model Predictive Control Progress in Systems and Control Theory (Том 26).

Control and Model-Based Analysis of Microaerobic Processes using Rhodospirillum rubrum as Model Organism. Number 6 in Contributions in Systems Theory and Automatic Control, Otto-von-Guericke Universität Magdeburg. Model Predictive Control for Nonlinear Networked Control Systems, A Model-based Compensation Approach for Nondeterministic Communication Networks. Number 5 in Contributions in Systems Theory and Automatic Control, Otto-von-Guericke Universität Magdeburg. Shaker Verlag, June 2014.

The book consists of selected papers presented at the International Symposium on Nonlinear Model Predictive Control †Assessment and Future Directions, which took place from June 3 to 5, 1998, in Ascona, Switzerland.

Nonlinear Model Predictive Control, or NMPC, is a variant of model predictive control (MPC) that is characterized by the use of nonlinear system models in the prediction. As in linear MPC, NMPC requires the iterative solution of optimal control problems on a finite prediction horizon. While these problems are convex in linear MPC, in nonlinear MPC they are not convex anymore  . Findeisen, Rolf; Allgower, Frank (2001). An introduction to nonlinear model predictive control". Summerschool on "The Impact of Optimization in Control", Dutch Institute of Systems and Control. Allgöwer; Zheng (2000). Nonlinear model predictive control. Progress in Systems Theory. Camacho; Bordons (2004).

Nonlinear model predictive control: Theory and algorithms. Grune . Pannek J. Download (PDF). Читать. Contemporary Trends in Nonlinear Geometric Control Theory and Its Applications. Carleman Estimates and Applications to Uniqueness and Control Theory (Progress in Nonlinear Differential Equations and Their Applications). Feruccio Colombini, Claude Zuily. Xiaoxin Liao, Pei Yu. Control Theory: Multivariable and Nonlinear Methods.

Model Predictive Control (MPC) (. Emergence of small computers with sufficient processing power. 2Nicolao, G. Magi, . and Scattolini, . Nonlinear Model Predictive Control, Vol. 26 of Progress in Systems and Control Theory, chap. Stability and Robustness of Nonlinear Receding Horizon Control, Birkh¨. auser, Basel-Boston-Berlin, 2000, pp. 3–22. 3Kerrigan, E. Robust Constraint Satisfaction Invariant Sets and Predictive Control, P. thesis, University of Cambridge, Department of. Engineering, November 2000. 4Dunbar, W. B. and Murray, . Model Predictive Control of Coordinated Multi-vehicle Formations, IEEE Conference on Decision and. Control (CDC), 2002, pp. 4631–4636.

Linear model predictive control approaches started appearing in the early eighties and are well-established in control practice (. Nonlinear model predictive control (NMPC) approaches started to appear about ten years later and have also found their way into control prac- tice (. though their popularity can not be compared to linear model predictive control. The use of Gaussian processes in modelling dynamic systems is a recent development . and some control algorithms based on such are described in. The chapter is organized as follows. Dynamic Gaussian process models are briefly introduced in the next section. 2. Allg¨ower . Zheng A. (ed., Nonlinear Model Predictive Control, Progress in system and control theory, Vol. 26, Birkh¨ auser Verlag, Basel, (2000).

During the past decade model predictive control (MPC), also referred to as receding horizon control or moving horizon control, has become the preferred control strategy for quite a number of industrial processes. There have been many significant advances in this area over the past years, one of the most important ones being its extension to nonlinear systems. This book gives an up-to-date assessment of the current state of the art in the new field of nonlinear model predictive control (NMPC). The main topic areas that appear to be of central importance for NMPC are covered, namely receding horizon control theory, modeling for NMPC, computational aspects of on-line optimization and application issues. The book consists of selected papers presented at the International Symposium on Nonlinear Model Predictive Control – Assessment and Future Directions, which took place from June 3 to 5, 1998, in Ascona, Switzerland.The book is geared towards researchers and practitioners in the area of control engineering and control theory. It is also suited for postgraduate students as the book contains several overview articles that give a tutorial introduction into the various aspects of nonlinear model predictive control, including systems theory, computations, modeling and applications.