A Simpler Adaptive Neural Network Tracking Control of Robot Manipulators by Output Feedback

Abstract

The trajectory tracking problem of a class of robot manipulators is investigated by a simpler design adaptive neural network(NN) in this paper. The Radial Basis Function(RBF) NN is utilized to handle the uncertainties of the dynamics. Compared with the traditional schemes, the dimension of the input vectors of RBFNN is decrease from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4n$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3n$</tex> but it have equal tracking and approximation performances. The output feedback control is considered when the velocity information cannot be obtained. Moreover, the weights of RBFNN converge to its optimal value by using the auxiliary filter to estimate weights error. The robot manipulator system is semi-globally and uniformly bounded which is proved by Lyapunov's theory. Simulation results demonstrate that the simpler controller has the same capability compared with the non-simplified method.