Efficient robust model predictive control for uncertain norm-bounded Markov jump systems with persistent disturbances via matrix partition

Yuchang Feng, Xue Li, Donglin Shi, Jun Ai

Abstract: In this paper, an efficient robust model predictive control scheme for the uncertain norm-bounded Markov jump system with persistent disturbances and physical constraints is proposed. The affine input control is applied to solve the state feedback gain matrices off-line and a new matrix partition method is considered to decrease the number of variables that need to be optimized on-line. The quadratic boundedness and the robust invariant ellipsoid set are used to guarantee the stochastic stability of the closed-loop augmented MJS. Therefore, the scheme advances the efficiency of on-line calculation and improves the control performance and the robustness of the closed-loop augmented MJS. Two numerical examples confirm the scheme.