Event-Sampled Output Feedback Control of Robot Manipulators Using Neural Networks

In this paper, adaptive neural networks (NNs) are employed in the event-triggered feedback control framework to enable a robot manipulator to track a predefined trajectory. In the proposed output feedback control scheme, the joint velocities of the robot manipulator are reconstructed using a nonlinear NN observer by using the joint position measurements. Two different configurations are proposed for the implementation of the controller depending on whether the observer is co-located with the sensor or the controller in the feedback control loop. Besides the observer NN, a second NN is utilized to compensate the effects of nonlinearities in the robot dynamics via the feedback control. For both the configurations, by utilizing observer NN and the second NN, torque input is computed by the controller. The Lyapunov stability method is employed to determine the event-triggering condition, weight update rules for the controller, and the observer for both the configurations. The tracking performance of the robot manipulator with the two configurations is analyzed, wherein it is demonstrated that all the signals in the closed-loop system composed of the robotic system, the observer, the event-sampling mechanism, and the controller are locally uniformly ultimately bounded in the presence of bounded disturbance torque. To demonstrate the efficacy of the proposed design, simulation results are presented.