Fuzzy weighting function dependent RHC design for TS fuzzy systems with input constraints

We present a state-feedback receding horizon control (RHC) for discrete-time fuzzy systems with input constraints. The models of the fuzzy systems are of Takagi-Sugeno type, which is suitable to model a large class of nonlinear systems. To find the control, we first derive an optimization problem involving parameterized linear matrix inequalities (PLMIs) that depend on the current and one-step past information on the time-varying fuzzy weighting functions. Since the PLMIs need to be checked for all values of the weighting functions, for solvability, we convert the PLMIs into a finite number of LMIs by suggesting a special structures for the variables of the PLMIs. Then, it is shown that the closed-loop system with the designed control is stable if the converted optimization problem is feasible at the initial time. A numerical example is presented to illustrate the performance of the controller.