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 paper investigates the design of a Model Predictive Control (MPC) formulation for the
case of polytopic multi-model system representation. An adaptive MPC is developed taking …

Temperature control in solar collectors is a nonlinear problem: the dynamics of temperature
rise vary according to the fluid flowing through the collector and to the temperature gradient …

In this paper, we propose a learning-based Model Predictive Control (MPC) approach for the
polytopic Linear Parameter-Varying (LPV) systems with inexact scheduling parameters (as …

Model predictive control (MPC) is one of the most effective methods of dealing with
constrained control problems. Nevertheless, the uncertainty of the control system poses …

A new Reinforcement Learning (RL) algorithm based on Model Predictive Control (MPC)
has been recently proposed in which the optimal state (-action) value function and the …

This paper explores the design of three different approaches of robust predictive control
formulations for the case of multi-model system representations. The first one is an optimum …

En general los procesos de la industria química son no lineales, lo que hace que los
algoritmos convencionales de control predictivo lineal resulten no factibles. Por lo tanto …