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 under a strict dissipativity assumption;

·        a rewritten chapter on distributed NMPC, in which a concrete distributed NMPC scheme is analyzed in detail; and

·        restructuring of central chapters away from presentation based on the order of their historical development to provide a more integrated treatment beginning with the general case of dissipativity-based NMPC (previously known as “economic” NMPC), updated with novel results followed by stabilizing NMPC schemes, both with and without stabilizing terminal ingredients.

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.

Lars Grüne has been a professor for Applied Mathematics at the University of Bayreuth, Germany, since 2002. He received his Diploma and Ph.D. in Mathematics from the University of Augsburg in 1994 and 1996, respectively, and his habilitation from the J.W. Goethe University in Frankfurt/M in 2001. He held or holds visiting positions at the Universities of Rome ‘Sapienza’ (Italy), Padova (Italy), Melbourne (Australia), Paris IX—Dauphine (France), Newcastle (Australia) and IIT Bombay (India). He has been a SIAM Fellow since 2025. Prof. Grüne was General Chair of the 25th International Symposium on Mathematical Theory on Networks and Systems (MTNS 2022), he is co-Editor-in-Chief of the journal Mathematics of Control, Signals, and Systems (MCSS) and is or was an associate editor of various other journals, including the Journal of Optimization Theory and Applications (JOTA), Mathematical Control and Related Fields (MCRF) and the IEEE Control Systems Letters (CSS-L).

Jürgen Pannek received his Ph.D. in mathematics from the University of Bayreuth, Germany, in 2010. Thereafter, he held postdoc positions in economics at Curtin University and The University of Western Australia, Australia, in 2010 and 2011. He joined the University of Federal Armed Forces Munich in 2012 as research assistant and substitute professor in aerospace engineering. In 2014, he became a professor in production engineering at the University of Bremen, Germany. From 2019 to 2021, he joined industry and became senior technical consultant responsible for building up a new robotics branch. In 2021, he joined TU Braunschweig, Germany, as head of the Institute for Intermodal Transport and Logistic Systems. His research is driven by development and application of control methods with a particular focus on system theory.

Timm Faulwasser is a full professor in the School of Electrical Engineering, Computer Science and Mathematics at Hamburg University of Technology, while before he held a professorship at TU Dortmund University. He has studied Engineering Cybernetics with a minor in philosophy at the University of Stuttgart (2000–2006). After doctoral studies in the International Max Planck Research School for Analysis, Design and Optimization in Chemical and Biochemical Process Engineering Magdeburg, he obtained his PhD from the Department of Electrical Engineering and Information Technology at Otto-von-Guericke University Magdeburg, Germany in 2012. Currently, he serves as associate editor for the IEEE Transactions on Automatic Control and Mathematics of Control Systems, and Signals. He received the 2021–2023 Automatica Paper Prize and the European Control Award 2025.