Adaptive Event-Triggered Control for Flexible Manipulators With Input Backlash and Prescribed Performance

This study presents an adaptive event-triggered control methodology for flexible manipulator systems with prescribed performance and input backlash. To reduce the communication burden between the controllers and actuators, we consider a relative threshold event-triggered mechanism. Then, an adaptive inverse function is applied to eliminate the input backlash of the actuator, and a neural network is adopted to handle the system uncertainty. It is proven that the proposed control approach not only ensures the tracking error converges to a small region close to zero within the prescribed time but also significantly reduces overshoot by using Lyapunov’s direct method. Furthermore, the efficacy of the scheme proposed is demonstrated through numerical simulations and experiments.