Motion Trajectory Error of Robotic Arm Based on Neural Network Algorithm

• Published in: Journal of control science and engineering Vol. 2023; pp. 1 - 6
• Main Authors: Xu, Bin, Sem-Lin, Chen
• Format: Journal Article
• Language: English
• Published: New York Hindawi 04.04.2023; John Wiley & Sons, Inc; Wiley
• Subjects: Adaptive control; Algorithms; Closed loop systems; Closed loops; Computer simulation; Control algorithms; Control methods; Control systems design; Controllers; Dynamic characteristics; Feedback control; Industrial production; Innovations; Liapunov functions; Manipulators; Manufacturing; Network control; Neural networks; Parameters; Positive feedback; Proportional integral derivative; Rehabilitation; Research methodology; Robot arms; Robot dynamics; Robotics; Robots; Simulation; Tracking errors; Trajectory control; China
• ISSN: 1687-5249; 1687-5257; 1687-5257
• DOI: 10.1155/2023/3958434

In order to solve the problems of unstable motion and large trajectory tracking error of the manipulator when it is disturbed by the outside world, the author proposes an adaptive neural network manipulator motion trajectory error method. The author gives the dynamic equation of the manipulator and uses the positive feedback neural network to study the dynamic characteristics of the manipulator. An adaptive neural network control system is designed, and the stability and convergence of the closed-loop system are proved by the Lyapunov function. A schematic diagram of the manipulator model is established, and MATLAB/Simulink software is used to simulate the dynamic parameters of the manipulator. At the same time, it is compared and analyzed with the simulation results of the PID control system. Simulation results show that in robot arm 3, the expected motion trajectory is θ3 = 0.4cos(2πt), the initial condition θ(0) = [000]τ, the control parameter K = diag(40,40),40), the disturbance parameter τ’ = 20cos(πt), robot arm link parameters l1 = 0.62 m, l2 = 0.41 m, l3 = 0.34 m, m1 = 3.5, m2 = 2.5 kg, m3 = 2 kg, g = 9.82 m/s2, under t = 2s, the motion trajectory of the robotic arm is disturbed by the outside world, and the adaptive neural network is used to control the motion trajectory with a small tracking error, input torque ripple is small. Conclusion. The manipulator adopts the adaptive neural network control method, which can improve the control accuracy of the motion trajectory and weaken the jitter phenomenon of the manipulator motion.