Neural network output feedback control of robot manipulators

A robust neural network output feedback scheme is developed for the motion control of robot manipulators without measuring joint velocities, A neural network observer is presented to estimate the joint velocities. It is shown that all the signals in a closed-loop system composed of a robot, an observer, and a controller is uniformly ultimately bounded. This amounts to a separation principle for the design of nonlinear dynamic trackers for robotic systems, The neural network weights in both the observer and the controller are tuned on-line, with no offline learning phase required. No exact knowledge of the robot dynamics is required so that the neural network controller is model-free and so applicable to a class of nonlinear systems which have a similar structure to robot manipulators. When compared with adaptive-type controllers, we do not require linearity in the unknown system parameters, or the tedious computation of a regression matrix, Simulation results on 2-link robot manipulator are reported to show the performance of the proposed output feedback control scheme.