One-step ahead robust MPC for LPV model with bounded disturbance

Abstract

The on-line model predictive control (MPC) approach usually assumes that with the system information been collected at each sampling instant, the control action can be calculated instantaneously. However, the practicality of this approach is limited by its ability to solve the optimization problem in real-time. In this paper, an improved on-line approach is proposed, where the controller parameters optimized from the previous sampling interval is utilized to calculate the current control action, i.e., the control action is implemented in a one-step ahead fashion. This one-step ahead approach solves the optimization problem during the sampling interval, which means that the controller and the real system are running in a concurrent manner. We first introduce the one-step ahead approach to state measurable case. Then, we extend this approach to state unmeasurable case, where the system output is utilized to estimate the system state. Furthermore, the recursive feasibility and stability are guaranteed for both cases. A numerical example is given to show the effectiveness of the proposed approach.