Adaptive Event-triggered Tracking Control for A Manipulator Based on Dynamic Neural Network

This paper is focused on the adaptive event-triggered tracking control of a manipulator subjecting to uncertain dynamics and unknown disturbance. To improve the accuracy of estimation and robustness, the adaptive control scheme based on disturbance observer is developed, in which the uncertain parameters are identified by an echo-state dynamic neural network. Moreover, the event-driven signal transportation mechanism is built in the channel of the controller to the actuator. Both stability and feasibility of the overall system under the proposed control scheme are verified based on Lyapunov theories. Finally, the simulation results demonstrate that our method could accomplish both motion tracking in a robust way, meanwhile could reduce communication.