Multi-strategy control to extend the feasibility region for robust model predictive control. (August 2022) - Oh, Tae Hoon; Kim, Jong Woo; Son, Sang Hwan; Jeong, Dong Hwi; Lee, Jong Min
Abstract: This paper proposes a multi-strategy control scheme, which modifies the optimal control problem of robust model predictive control (RMPC) to reduce the on-line computational load or extend the feasible region. The proposed controller is designed to stabilize the system with respect to a subset of the disturbance set. If the disturbance is realized from the rest of the subset, another control strategy is automatically involved to keep the state inside the pre-determined bounded set. The existence of this pre-determined set is proven, and an efficient algorithm is proposed to generate this set. In addition, it is shown that the recursive feasibility and stability of the original RMPC is sustained for the proposed controller. This implies that the proposed method can be applied to a wide range of existing RMPC. Three illustrative examples describe the fundamental ideas and practical advantages. Highlights: A multi-strategy control scheme is proposed to reduce the on-line computational load of robust model predictive control. The extension of the feasibility set of the proposed robust model predictive control is proven. Both the recursive feasibility and stability of the proposed robust model predictive control is proven. An efficient algorithm that generates the feasibility set of the proposed robust model predictive control is proposed.