Modeling of Robot's Low-Speed Motion Nonlinear Dynamics Based on Phase Space Reconstruction Neural Network

Abstract

Abstract In order to improve the accuracy of the robot dynamics model, a low-speed motion nonlinear dynamics modeling method of industrial robot based on phase space reconstruction neural network is proposed. It is confirmed in advance by the largest Lyapunov exponent of joint motor torque data that the robot has chaotic characteristics at low-speed motion. Therefore, experimental data and chaos theory is used to analyze low-speed motion nonlinear dynamics, instead of considering each factor that may cause the robot's nonlinear dynamics separately. The phase space reconstruction parameters of each joint are determined by autocorrelation method and false nearest neighbor method. Through data preprocessing and analysis, some joint position derivatives related to the changing law of torque data are determined. The phase space reconstruction values of these derivatives are chosen as the inputs of neural network. Then the neural network and curve fitting method are combined to compensate for the nonlinear joint torque. Experimental results show that the proposed method can better describe the robot's low-speed motion nonlinear dynamics, and has smaller errors compared with ordinary back propagation (BP) neural network in the case of single joint rotation.