Manipulator Control Based on Adaptive RBF Network Approximation

Abstract

With the popularization of intelligent manufacturing, manipulator has found ever wider application in various industries. A manipulator requires a real-time and fast control algorithm in order to improve the accuracy in all kinds of precise operations. This paper proposes an algorithm based on adaptive radial basis function (RBF) for approximating the parameters of the manipulator, and the adaptive equations are designed to automatically adjust the weight of RBF. Proportional integral (PI) robust based on dynamic error tracking is used in controller to reduce the steady state errors and enhance the anti-interference performance of the system. The global asymptotic stability of the system is demonstrated by defining an integraltype Lyapunov function. Finally, MATLAB is used to simulate the angular positions tracking and angular velocities tracking of the double joints manipulator. The results show that the manipulator can track the ideal output signal quickly and accurately and has good anti-interference performance.