Robust Fuzzy Model Predictive Controller Design for Uncertain Nonlinear Systems

Abstract

This paper proposes a synthetic scheme for the robust fuzzy predictive controller design of uncertain nonlinear systems. First, the Takagi-Sugeno (T-S) fuzzy model is employed to modeling the uncertain nonlinear systems. Next, combining robust stability concept, T-S fuzzy model approach, and model predictive control (MPC) strategy, at each sampling time, the predictive state feedback controller is calculated by minimizing a least upper bound for MPC infinite horizon objective function. Sufficient conditions, satisfying the input constraints, are also derived for robust asymptotic stability and can be reduced into linear matrix inequalities (LMIs). Finally, a truck-trailer system which has unstable and nonlinear with time varying parameter is applied for backing-up control test. The simulation result demonstrates that the proposed approach can achieve the robust fuzzy MPC objective.