Dynamic Modeling of Industrial Robot Based on Support Vector Machine Regression Algorithm

Abstract

The industrial robot is a highly nonlinear and highly coupled device, and the calculation of the dynamic model needs to spend a lot of time, which result in difficult to achieve dynamic control. Using of intelligent algorithm to model the robot dynamics has been the field of robotics research focus. This paper proposes a method based on support vector machine regression algorithm to model the robot dynamics. The angle acceleration coefficient, and the centripetal acceleration and Coriolis acceleration coefficient, and the gravity in the robot dynamics equation are predicted based on support vector machine regression. When the support vector machine is trained, parameters in the robot dynamics equation don’t rely on the position of each joint any more. Modeling process was described in detail by using a two degree of freedom robot. All the parameters are simulated. Simulation results show that the method has the characteristic of high precision, and short training time.