Hybrid Event-Triggered Adaptive Control for Nonlinear Systems with Dynamic Uncertainties and Unknown disturbances

Abstract

This paper focuses on the tracking control problem for nonlinear systems subject to dynamic uncertainties and external disturbances. First, the neural network is utilized to estimate the system uncertainties and the adaptive nonlinear disturbance observer (NDO) is established to detect and compensate environmental disturbances. Then, the differentiator is constructed, the adaptive controller is designed, and the hybrid event-triggering mechanism is introduced to reduce the energy consumption, balance the security of system and the fineness of control. Combined with Lyapunov stability theory, we show that the discussed closed-loop signals are all uniformly bounded. In addition, the Zeno behaviour is successfully excluded. The practicability and reliability of the designed control strategy are proved by a numerical simulation case.