Nonlinear Model Predictive Control (eBook, PDF)

Redaktion: Allgöwer, Frank; Zheng, Alex

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Birkhäuser Basel
• Seitenzahl: 472
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9783034884075
• Artikelnr.: 53181800

Inhaltsangabe

I Theoretical Issues in Nonlinear Predictive Control.- Stability and Robustness of Nonlinear Receding Horizon Control.- Nonlinear Model Predictive Control: Challenges and Opportunities.- Nonlinear Horizon State Estimation.- Predictive Control of Constrained Hybrid Systems.- Stability, Feasibility, Optimality and the Degrees of Freedom in Constrained Predictive Control.- A Predictive Command Governor for Nonlinear Systems under Constraints.- Some Practical Issues and Possible Solutions for Nonlinear Model Predictive Control.- Nonlinear Model Predictive Control for Index-one DAE Systems.- Analytical Predictive Control.- Integrating Predictive and Switching Control: Basic Concepts and an Experimental Case Study.- Exploring the Potentiality of Using Multiple Model Approach in Nonlinear Model Predictive Control.- Continuous-time Predictive Control of Constrained Nonlinear Systems.- II Modelling and Computational Aspects in Nonlinear Predictive Control.- Efficient Solution of Dynamic Optimization and NMPC Problems.- A Direct Multiple Shooting Method for Real-time Optimization of Nonlinear DAE Processes.- Modeling and Identification for Nonlinear Model Predictive Control: Requirements, Current Status and Future Research Needs.- Structural Concepts for Optimization Based Control of Transient Processes.- Efficient Nonlinear Modeling Using Wavelet Compression.- Iterative Active-set Method for Efficient On-line MPC Design.- Nonlinear Predictive Control Algorithms with Different Input Sequence Parametrizations Applied for the Quadratic Hammerstein and Volterra Models.- Nonlinear Model Predictive Control Based on Stable Wiener and Hammerstein Models.- III Applications of Nonlinear Predictive Control.- An Overview of Nonlinear Model Predictive Control Applications.- Multi-zone Control under Enterprise Optimization: Needs, Challenges and Requirements.- Nonlinear Model Predictive Control of a Styrene Polymerization Reactor.- Nonlinear Multi-rate MPC with Large Scale Fundamental Models: Application to a Continuous Kamyr Digester.- Multivariable Control of Cement Mills.- Nonlinear Receding Horizon Control of Internal Combustion Engines.- Performance and Computational Implementation of Nonlinear Model Predictive Control on a Submarine.

Rezensionen

"[A] skillfully assembled and thoroughly reviewed collection of papers...containing some of the latest developments in a rapidly developing area.... To conclude, to anybody interested in nonlinear predictive control, the collection of papers edited by Frank Allgöwer and Alex Zhang is a good starting point. To anybody working on nonlinear predictive control, the collection is a source of inspiration and [a] gold mine of problems." -Control and Cybernetics