Nonlinear Model Predictive Control: Theory and Algorithms - Lars Grüne

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. 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, 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:

Though primarily aimed at academic researchers and practitioners working in control and optimization, Nonlinear Model Predictive Control (third edition) is self-contained, featuring background material on infinite-horizon optimal control and Lyapunov stability theory, which also makes it accessible for graduate students in control engineering and applied mathematics.