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++, as well as many examples from the book implemented in Maple, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. Files are available from http://www.nmpc-book.com.
