Networked Adaptive Backstepping Control for a Class of Strict-Feedback Nonlinear Systems Using Event-Triggered Output and Control Signals

This paper proposes a fuzzy-observer-based adaptive backstepping control strategy for a class of strict-feedback nonlinear systems by introducing an event-triggered mechanism in both sensor-to-controller and controller-to-actuator channels to save network resources. By designing a fuzzy observer to obtain continuous-time state estimates, the non-differentiability problem of virtual control signals caused by event-triggered mechanisms can be effectively resolved. The event-triggered adaptive backstepping controller is designed by using continuous estimation state of the fuzzy observer. It is proved that all signals of closed-loop system are semi-global uniform ultimate bounded and Zeno behavior is avoided. Simulation results validate the effectiveness of the proposed control strategy.