Three decades have passed since milestone publications by several industrialists spawned
a flurry of research and industrial/commercial activities on model predictive control (MPC) …

This paper presents a review on the development and application of model predictive
control (MPC) for autonomous intelligent mechatronic systems (AIMS). Starting from the …

This paper presents a method to compute an approximate explicit model predictive control
(MPC) law using neural networks. The optimal MPC control law for constrained linear …

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 …

Explicit model predictive control (MPC) addresses the problem of removing one of the main
drawbacks of MPC, namely the need to solve a mathematical program on line to compute …

Motivated by the fact that many nonlinear plants can be represented through Linear
Parameter Varying (LPV) embedding, and being this framework very popular for control …

This article presents a computationally efficient and high performing approximate long-
horizon model predictive control (MPC) for permanent magnet synchronous motors …

Limits on the storage space or the computation time restrict the applicability of model
predictive controllers (MPC) in many real problems. Currently available methods either …

An algorithm for the construction of an explicit piecewise linear state feedback
approximation to nonlinear constrained receding horizon control is given. It allows such …

In this article, we propose a novel framework for approximating the MPC policy for linear
parameter-varying systems using supervised learning. Our learning scheme guarantees …