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Nonlinear Model Predictive Control: Theory and Algorithms (Communications and Control Engineering) [Repost]
![Nonlinear Model Predictive Control: Theory and Algorithms (Communications and Control Engineering) [Repost]](https://pixhost.icu/avaxhome/b5/f8/004ef8b5.jpg)
Nonlinear Model Predictive Control: Theory and Algorithms (Communications and Control Engineering) by Lars Grüne
English | 22 Nov. 2016 | ISBN: 3319460234 | 456 Pages | PDF | 9.67 MB
English | 22 Nov. 2016 | ISBN: 3319460234 | 456 Pages | PDF | 9.67 MB
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.