Neural network output feedback control of robot manipulators

Abstract

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 online, with no off-line 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. Simulation results on 2-link robot manipulator are reported to show the performance of the proposed output feedback control scheme.