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 Specification of Software Systems by Lars Grüne, Jürgen Pannek
Cover of the book Stochastic Analysis of Offshore Steel Structures by Lars Grüne, Jürgen Pannek
Cover of the book Minimally Invasive Surgery for Achilles Tendon Disorders in Clinical Practice by Lars Grüne, Jürgen Pannek
Cover of the book Analysis and Control of Boolean Networks by Lars Grüne, Jürgen Pannek
Cover of the book Wind Energy Conversion Systems by Lars Grüne, Jürgen Pannek
Cover of the book Mobile Persuasion Design by Lars Grüne, Jürgen Pannek
Cover of the book Training in Minimal Access Surgery by Lars Grüne, Jürgen Pannek
Cover of the book Multicore Programming Using the ParC Language by Lars Grüne, Jürgen Pannek
Cover of the book Organic Solar Cells by Lars Grüne, Jürgen Pannek
Cover of the book Osteoporosis Research by Lars Grüne, Jürgen Pannek
Cover of the book Guide to ILDJIT by Lars Grüne, Jürgen Pannek
Cover of the book Management of Fractures in Severely Osteoporotic Bone by Lars Grüne, Jürgen Pannek
Cover of the book Applications and Innovations in Expert Systems VI by Lars Grüne, Jürgen Pannek
Cover of the book Clinical Approach to Sudden Cardiac Death Syndromes by Lars Grüne, Jürgen Pannek
Cover of the book Brain CT Scans in Clinical Practice 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