Predictor-Based Periodic Event-Triggered Adaptive Fuzzy Control for a Class of Nonlinear Systems

Addressed in this article is the problem of observer-based periodic event-triggered (PET) adaptive fuzzy control for a class of nonlinear systems adopting a predictor approach. In most existing results on PET output-feedback control, only the system output at sampling instants is available for observer design. For the use of more effective information in observer, a predicted signal of the continuous system output is generated by a predictor and used for the construction of observer. Then, a dynamic gain is introduced to the construction of Lyapunov function to ensure the decreasing property of the developed Lyapunov function at sampling instants. Owing to this good property, the PET adaptive controller can be designed using adaptive fuzzy control and backstepping techniques. It is proved that the system states are locally bounded and converge to a small neighborhood of the origin under given sampling period and controller. Finally, two examples are provided to verify the effectiveness and advantage of the proposed control method.