We show that artificial neural networks with rectifier units as activation functions can exactly
represent the piecewise affine function that results from the formulation of model predictive …

This paper provides an overview of the recent research efforts on the integration of machine
learning and model predictive control under uncertainty. The paper is organized as a …

We consider the problem of synthesizing simple explicit model predictive control feedback
laws that provide closed‐loop stability and recursive satisfaction of state and input …

Faster execution times achievable with explicit model predictive control (EMPC) promise to
further extend the applicability of MPC. This work presents a novel approach for developing …

In this article, a robust nonlinear model predictive control (NMPC) scheme with two control
loops is considered and its real‐time execution is guaranteed for a predefined sampling …

The universal approximation property of Takagi-Sugeno fuzzy systems is exploited here to
build a fuzzy approximation of the optimal solution of linear and nonlinear model predictive …

In this chapter, we argue that model predictive control (MPC) can be a very powerful
technique to mitigate some of the challenges that arise when designing and deploying …

Abstract Model predictive control (MPC) has established itself as the standard method for the
control of complex nonlinear systems due to its ability to directly consider constraints and …