This paper investigates the reliable stabilization and L2−L∞ performance control problem for discrete-time descriptor systems described by Takagi–Sugeno (T-S) fuzzy models under stochastic data dropouts and actuator faults. In view of the practical situation that system states are usually unmeasurable, a novel observer-based proportional–derivative (PD) control strategy is proposed. Different from traditional state feedback, the PD structure effectively alleviates the inherent structural constraints of descriptor systems and relaxes the conditions for system regularity and causality. By constructing a parameter-dependent Lyapunov functional and using the Schur complement lemma, sufficient conditions are derived in the form of linear matrix inequalities (LMIs) to guarantee the stochastic stability of the closed-loop system and the prescribed L2−L∞ performance. The effectiveness and superiority of the proposed methodology are verified through extensive numerical simulations on two practical case studies, namely, a bio-economic system and a DC motor system. In the case of actuator faults and data dropouts the observer achieves accurate state tracking, and the peak value of the system output is strictly constrained. The research results confirm that the method has strong robustness against data dropouts and actuator faults.
Yang et al. (Sun,) studied this question.