FUZZY ROBUST CONSTRAINED MODEL PREDICTIVE CONTROL FOR NONLINEAR SYSTEMS

This paper addresses robust constrained model predictive control (MPC) for a class of nonlinear systems with structured time-varying uncertainties. First, the Takagi-Sugeno (T-S) fuzzy model is employed to represent a nonlinear system. Then, we develop some techniques for designing fuzzy control which guarantees the system stabilization subject to input and output constraints. Both parallel and nonparallel distributed compensation control laws (PDC and non-PDC) are considered. Sufficient conditions for the solvability of the controller design problem are given in the form of linear matrix inequalities. A simulation example is presented to illustrate the design procedures and performances of the proposed methods.