PD Control of Robot Manipulators with Uncertainties Based on Neural Network

Abstract

This paper brings forward two kinds of PD control schemes of adaptive neural-variable structure for uncertain robot trajectory tracking. The first scheme consists of a PD feedback and a dynamic compensator which is composed of RBF neural network and variable structure. The adaptive laws of Network weights are based on Lyapunov function method. This controller can guarantee stability of closed-loop system and asymptotic convergence of tracking errors. The second scheme substitutes the integrated controller consisting of neural network and variable structure for the hybrid controller by way of smooth function. This integrated controller can reduce chattering of variable structure control input, overcome the deficiencies of local generalization neural networks and improve control precision and convergence speed. In addition, This controller is still able to ensure the system maintains good robustness and stability in the case of neural network disabled. The simulation results have showed the effectiveness of two kinds of control schemes, and that the second scheme is more advantageous.