Nonlinear Model Predictive Control

This book 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 sub-optimality can be derived in a uniform manner. These results are complemented by discussions of feasibility, robustness, stochastic and distributed NMPC. 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 yields essential insights into how the nonlinear optimization routine the core of any nonlinear model predictive controller works. Accompanying software in MATLAB® and Python (downloadable from link.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC.

The third edition has been substantially rewritten, edited and updated to reflect recent significant advances, including:

· a new chapter on data-driven NMPC, detailing an approach using the Koopman operator;

· new sections on stochastic dissipativity-based NMPC, which provide a comprehensive theory and allow derivation of a hierarchy of performance and stability statements;

· new results on the analysis of infinite-horizon optimal control problems u