A neural network adaptive control scheme for robot manipulators

A neural network adaptive control scheme for robot manipulators is proposed which consists of one Adaline network to identify structured system dynamics and another one to compensate for both structured and unstructured dynamic uncertainties. The former is trained offline using an LMS (least mean square) type algorithm, while the latter uses an online stable weight updating mechanism determined using Lyapunov theory. Since Adaline nets match robot regressor dynamics perfectly, the training processes of the resulting simple neural networks are computationally efficient and the proposed adaptive control scheme has very high potential in real-time applications. The proposed control scheme is illustrated by simulation and comparison studies