Event-Triggered Adaptive Sliding Mode Control for Robotic Arms with Predefined-Time Convergence

Abstract

To handle the issue of trajectory tracking control for a robotic manipulator affected by unknown model dynamics and external perturbations, an adaptive predetermined-time event-triggered control algorithm is proposed. A novel piecewise continuous predefined-time sliding mode function is introduced, ensuring that the system state converges to the sliding surface within the predetermined time while avoiding singularity issues. Based on this sliding mode function, an adaptive neural network update law is designed to compensate for model uncertainties and external disturbances. Additionally, an event-triggered sliding mode controller is developed, which not only achieves predefined-time tracking control but also reduces the frequency of control signal updates, thereby conserving communication resources. Theoretical analysis confirms that the tracking error rapidly decreases and stabilizes within a vicinity of the origin in a finite predetermined time, and the controller avoids the Zeno phenomenon. Finally, the simulation results validate the effectiveness and advantages of the proposed control strategy.