Event-Triggered Prescribed Performance Control for Nonlinear Servo Systems with Unknown Disturbances

This paper investigates the tracking control problem of servo systems afflicted by unknown nonlinearities and external disturbances. To tackle this issue, a prescribed performance control (PPC) strategy incorporating neural network compensation is proposed, accompanied by the design of an event-triggering mechanism to significantly optimize communication resources utilization. Initially, a performance function is introduced with the objective of constraining system tracking error and facilitating error transformation. Subsequently, a neural network is developed to estimate and compensate for the lumped unknown dynamics. By ensuring both system performance and stability, an event-triggering mechanism is devised for conserving system resources while achieving the desired tracking objective. Furthermore, it is demonstrated that the system signal remains bounded, and the proposed triggering mechanism exhibits no Zeno behavior. Finally, simulation results validate the effectiveness of the algorithm.