Event-Triggered Adaptive Neural Tracking Control of Flexible-Joint Robot Systems With Input Saturation

This paper investigates an event-triggered adaptive neural tracking control issue for flexible-joint robot (FJR) systems subject to unknown dynamic and input saturation. To enable the backstepping design framework to be implemented, the input saturation nonlinearity is replaced by a smooth function. In the control design, the dynamic surface control (DSC) and adaptive neural techniques are used to handle the "explosion of complexity" issue and unknown dynamics, respectively. Furthermore, to reduce the calculated burden caused by the adaptive neural reconstruction technique, three virtual parameters are updated by using the single-parameter-learning approach. To decrease the frequency of actuator response to the control command for reducing the mechanical wear of actuator, an event triggering mechanism is established between the control law and actuator. Finally, an event-triggered adaptive neural tracking control solution is proposed, which is endowed the advantages as: (1) it does not need any priori knowledge of FJR systems; 2) it only needs to update three unknown parameters; 3) it can reduce the transmission frequency of the control commands and the response rate of the actuator. Using the Lyapunov stability theory, the proposed event-triggered control solution ensures that all signals of the closed-loop tracking control system are bounded. Finally, the simulation results verify the effectiveness and superiority of the proposed control scheme.