Nonlinear Model Predictive Control

Theory and Algorithms

Nonfiction, Science & Nature, Science, Other Sciences, System Theory, Technology, Automation
Cover of the book Nonlinear Model Predictive Control by Lars Grüne, Jürgen Pannek, Springer London
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Lars Grüne, Jürgen Pannek ISBN: 9780857295019
Publisher: Springer London Publication: April 11, 2011
Imprint: Springer Language: English
Author: Lars Grüne, Jürgen Pannek
ISBN: 9780857295019
Publisher: Springer London
Publication: April 11, 2011
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer London

Cover of the book Frontiers of Human-Centered Computing, Online Communities and Virtual Environments by Lars Grüne, Jürgen Pannek
Cover of the book Evaluation of Cancer Screening by Lars Grüne, Jürgen Pannek
Cover of the book Topics in Physical Mathematics by Lars Grüne, Jürgen Pannek
Cover of the book Recent Advances in the 3D Physiological Human by Lars Grüne, Jürgen Pannek
Cover of the book Creativity in the Digital Age by Lars Grüne, Jürgen Pannek
Cover of the book Illustrated Dictionary of Practical Astronomy by Lars Grüne, Jürgen Pannek
Cover of the book A Neural Network Approach to Fluid Quantity Measurement in Dynamic Environments by Lars Grüne, Jürgen Pannek
Cover of the book Diseases in the Homosexual Male by Lars Grüne, Jürgen Pannek
Cover of the book Guide to Medical Image Analysis by Lars Grüne, Jürgen Pannek
Cover of the book Finitely Generated Abelian Groups and Similarity of Matrices over a Field by Lars Grüne, Jürgen Pannek
Cover of the book Technopolis by Lars Grüne, Jürgen Pannek
Cover of the book Advanced Network Programming – Principles and Techniques by Lars Grüne, Jürgen Pannek
Cover of the book Offshore Medicine by Lars Grüne, Jürgen Pannek
Cover of the book Essentials of Autopsy Practice by Lars Grüne, Jürgen Pannek
Cover of the book Visual Analysis of Behaviour by Lars Grüne, Jürgen Pannek
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy