Output feedback control of rigid robots using dynamic neural networks

Abstract

A robust neural network (NN) output feedback scheme is proposed for the motion control of rigid robots. A dynamic NN observer is presented to estimate the joint speeds. The stability of a closed-loop system composed of a robot, a NN observer, and a NN controller is proven. The NN weights in both the observer and the controller are tuned online, with no off-line learning phase required. Most importantly, we can guarantee the boundness of the estimated velocities, the position tracking errors, and the NN weights. Also no exact knowledge of the robot dynamics is required so that the NN controller is model-free and so applicable to any type of rigid robot. When compared with adaptive-type controllers, we do not require persistent excitation conditions, linearity in the unknown system parameters, or the tedious computation of a regression matrix. Thus the new NN approach represents an improvement over adaptive techniques.