Accuracy Improvement of Robot Arm Trajectory based on Adaptive Neural Network Algorithm

An adaptive neural network control method was proposed to solve the problems such as unstable motion and large trajectory tracking error when the robot arm was disturbed by the external environment.The dynamic equations of the manipulator were given and the dynamic characteristics of the manipulator were studied by using the positive feedback neural network. Then the adaptive neural network control system was designed, and the stability and convergence of the closed-loop system were proved by the Lyapunov function. Later, the model diagram of the robot arm was established, and the dynamics parameters of the manipulator were simulated by MATLAB /Simulink software.At the same time, they were compared with the simulation results of the PID control system for analysis.The simulation results showed that the trajectory tracking error and input torque fluctuation were smaller when the trajectory of the robot arm was disturbed by the external world. When adopting the control method of the adaptive neural network, the robot arm could improve the control precision of the trajectory, thus reducing the jitter of the robot arm motion.