Neural network control of flexible-joint robot manipulators

In this paper, for flexible-joint robot manipulators with weak flexibility, we propose a neural network trajectory-tracking strategy based on singular perturbation theory. Under general assumptions, we prove that the tracking error is ultimately uniformly bounded and that the corresponging ultimate bound can be sufficiently decreased by modifying the feedback gain matrix. Since the linearization assumption of the unknown parameters is removed, the regression matrix need not be conmputed. Therefore, the proposed method has great robustness and the ability of model generalization. The numerical simulation shows that the proposed method is feasible and efficient.