Design of Robust Adaptive Neural Switching Controller for Robotic Manipulators with Uncertainty and Disturbances

In this paper, we present the robust adaptive neural switching control problem for the application of robotic manipulators with uncertainty and disturbances. The control scheme relaxes the hypothesis that the bounds of external disturbance and approximation errors of neural networks are known. RBF Neural Networks (Radial Basis Function NNs) are adopted to approximate unknown functions of robotic manipulators and an H∞ controller is designed to enhance system robustness and stabilization due to the existence of the compound disturbance which consists of approximation errors of the neural networks and external disturbance. The adaptive updated laws and the admissible switching signals have been derived from switched multiple Lyapunov function method, so that both system tracking stability and error convergence can be guaranteed in the closed-loop system. Experimental results have demonstrated the improved performance of the proposed control scheme over PD (Proportional Differential) control strategy, which have shown good accuracy of position tracking.