Fixed-TimeAdaptive Neural Control for Strict Feedback Nonlinear Systems via Event-Triggered Mechanism

This article investigates an event-trigger-based fixed-time adaptive tracking control problem for strict feedback nonlinear systems with external disturbances. Relying on the values of the control input and tracking error, the relative fixed event-triggered control scheme is introduced to save communication resources on the basis of ensuring the control effect. Based on backstepping technology and neural network schemes, a fixed-time controller is devised to certify that the tracking error converges to a small neighborhood of the origin in the settling time. Meanwhile, all the signals of the closed-loop systems are bounded. The simulation results are presented to demonstrate the effectiveness of the proposed method.