An adaptive model predictive control strategy for a class of discrete‐time linear systems with parametric uncertainty

Abstract

In this study, an adaptive model predictive control (MPC) strategy is proposed for a class of discrete-time linear systems with parametric uncertainty. In the presented adaptive MPC, an updating law is firstly designed to update the estimated parameters online. By utilizing the estimated parameters, a standard MPC optimization problem for unconstrained systems is established. Then, to deal with constrained systems, the min-max MPC technique is developed under the set-based approach for the estimated parameters. Furthermore, it is shown theoretically that the recursive feasibility and closed-loop stability can be rigorously proved, respectively. Finally, numerical simulations and comparative analysis are presented to illustrate the superiority of the proposed algorithms in control performance.

Robust fuzzy model predictive control for nonlinear discrete‐time systems

“…Model Predictive Control (MPC) as a sort of optimization control approach has attracted numerous attention in the industry. 1,2 Commonly, an MPC anticipates the near future of the system under consideration and optimizes a finite horizon cost function regarding specific constraints, while ensuring stability and recursive feasibility. An optimal control sequence is achieved by adopting recently existing information on the system but only the first control law is implemented.…”