Event-Triggered Adaptive Control for Robotic Manipulators via Identifier–Critic Reinforcement Learning

This paper investigates reinforcement learning (RL)-based tracking control for robotic manipulators under event-triggered mechanisms. First, the dynamic model of the manipulator is formulated, accounting for model uncertainties and external disturbances. Then a super-twisting sliding mode surface is designed and embedded into the RL control framework. To minimize tracking errors and control efforts, a performance index function is constructed, and an identifier-critic RL framework is employed to approximate the optimal control policy. An event-triggered mechanism is introduced to update control commands only upon event occurrences defined by a triggering rule, thus reducing communication overhead. Theoretical analysis establishes closed-loop stability and proves the absence of Zeno behavior by ensuring a strictly positive lower bound on the inter-event time. Simulations and experiments are conducted to validate the effectiveness of the proposed strategy.