Design of Picking Robot Manipulator Control System Based on Fuzzy Compensation RBF Neural Network

Abstract

Industrial manipulator occupies a very important position in industrial production. The tracking control of its control system and joint trajectory has always been a research hotspot. But the manipulator is a multi input multi output system, which has the characteristics of nonlinearity and strong coupling. Radial basis function (RBF) neural network has high nonlinear mapping ability. In this paper, the structure characteristics, learning algorithm and application of RBF neural network in manipulator control are analyzed. In this paper, the nonlinear approximation property of RBF neural network is theoretically verified. This paper analyzes the basic structure of picking manipulator system in detail. At the same time, the Lagrange Euler method is used to deduce the dynamic equation of the two degree of freedom series manipulator, and the inertia characteristics, Coriolis force and centripetal force characteristics, heavy torque characteristics are analyzed. The nonlinear system model of manipulator based on S-function is established in MATLAB, and the dynamic model is transformed into the form of second-order differential equation to facilitate the introduction of the designed algorithm.