Abstract
This paper proposes an observer-based dynamic event-triggered adaptive control approach for uncertain nonlinear strict-feedback systems. Initially, an observer for unmeasurable states is constructed. Subsequently, employing backstepping technique, the output-feedback adaptive control law and parameter adaptive law are developed. The tuning function method is used to avoid the over-parameterization problem in parameter adaptive law design. To lower the data exchange rate within the network, a dynamic event-triggered scheme is formulated to allow real-time update control signal. Through Lyapunov theory analysis, the resulting closed-loop system is demonstrated to be stabilized, and the occurrence of Zeno behavior is effectively prevented. Finally, the simulation example validates the obtained result of the presented design approach.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have