PD Control of Robot Manipulators with Uncertainties Based on Neural Network

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.

I. Introduction

Over the past decade, The trajectory tracking control problem of robot has attracted more and more attentions. However, since it is itself a time-varying, strong-coupling and non-linear system, and too a system which contains many traits such as parameter errors, unmodeled dynamics external interference as well as various other unknown non-linear in the actual project, the traditional control schemes is difficult to effectively control the robot due to their poor robustness and control precision. Therefore, a variety of intelligent control based on nonlinear compensation method [1]~[3]are continuously put forward in recent years.