Robust adaptive control of robots using neural network: global tracking stability

Abstract

A desired compensation adaptive law-based neural network (DCAL-NN) controller is proposed for the robust position control of rigid-link robots. The NN is used to approximate a highly nonlinear function. The controller can guarantee the global asymptotic stability of tracking errors and boundedness of NN weights. In addition, the NN weights here are tuned on-line, with no off-line learning phase required. When compared with standard adaptive robot controllers, one does not require persistent excitation conditions, linearity in the parameters, or lengthy and tedious preliminary analysis to determine a regression matrix. The controller can be regarded as a universal reusable controller because the same controller can be applied to any type of rigid robots without any modifications.