Neural-variable structure-based adaptive trajectory tracking control of robot manipulators

The trajectory tracking of a class of robot manipulators with uncertainties is considered. The syncretic control algorithm is proposed by adaptive neural network and variable structure. Neutral network is used to adaptively learn and compensate the unknown system, and approach error as disturbance is eliminated by using variable structure controller. Considering the shortage of local network, based on partition of state dimensional, neural network and variable structure separate control is applied to three sections with classification and integration. Two controllers together keep the robust of system in control initial stages and outside of approach region. The controller can guarantee good robustness and the stability of closed-loop system based on Lyapunov. The simulation results show the effectiveness of the presented methods.