Constrained model predictive control for T-S fuzzy system with randomly occurring actuator saturation and packet losses via PDC and non-PDC strategies

This paper studies model predictive control for a Takagi-Sugeno (T-S) fuzzy system with randomly occurring actuator saturation and packet losses. The nonlinearity of the actuator saturation is transformed into a set of convex hulls, while the packet losses are assumed to obey the rules of Bernoulli distribution. Both parallel-distributed-compensation (PDC) and non-parallel-distributed-compensation (non-PDC) strategies are adopted to design the controller for the system. In addition, sufficient conditions of the stability for the closed-loop system are given in terms of linear matrix inequalities. It is shown that the non-PDC strategy behaves less conservatively than the PDC strategy in controlling the considered T-S fuzzy system, when the input and output constraints are explicitly considered. Two simulation examples are provided to illustrate the effectiveness of the proposed design techniques.