This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models.

This book gives an up-to-date assessment of the current state of the art in the new field of nonlinear model predictive control (NMPC).

With a simple approach that includes real-time applications and algorithms, this book covers the theory of model predictive control (MPC).

Controlling a system with control and state constraints is one of the most important problems in control theory, but also one of the most challenging.

This book presents computationally efficient MPC solutions.

... algorithm for convex optimization. IEEE Transactions on Automatic Control (2018). DOI 10.1109/TAC.2018.2872203 Stella, L., Themelis, A., Sopasakis, P., Patrinos, P.: A simple and efficient algorithm for nonlinear model predictive control ...

The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors.

Mixed-integer nonlinear programming (MINLP) problems combine the numerical difficulties of handling nonlinear functions with the challenge of optimizing in the context of nonconvex functions and discrete variables.

... control problems. References Toward Global Search for Local Optima Jens Deussen, Jonathan Hüser,. 1. Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm ... efficient algorithm for nonlinear model predictive control ...

... A simple and efficient algorithm for nonlinear model predictive control, in: 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017, pp. 1939–1944. This page intentionally left blank Chapter 5 Perception capabilities for ...