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).

This book considers the multi-parametric Nonlinear Programming (mp-NLP) approaches to explicit approximate NMPC of constrained nonlinear systems, developed by the authors, as well as their applications to various NMPC problem formulations ...

This book presents computationally efficient MPC solutions.

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

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 focuses on distributed and economic Model Predictive Control (MPC) with applications in different fields.

Nonlinear Model Predictive Control is primarily aimed at academic researchers and practitioners working in control and optimisation but the text is self-contained featuring background material on infinite-horizon optimal control and ...

... 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 ...