Adaptive tube‐based model predictive control for linear systems with parametric uncertainty

Abstract

A tube-based robust model predictive control (MPC) is proposed to be applied in constrained linear systems with parametric uncertainty. An estimation method is applied in this proposed technique to adapt the system model at each sampling time and to reduce the conservatism nature of the tube-based MPC as the system model approaches the real model as time passes. By updating the subject model online through this newly proposed approach the performance of the system is improved. Asymptotic stability of the closed-loop system is established. The simulation results of a DC motor are applied to illustrate the effectiveness of this proposed controller in dealing with one practical system.