Motion Trajectory Error of Robotic Arm Based on Neural Network Algorithm
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...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/3958434 |
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| _version_ | 1849766971268136960 |
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| author | Bin Xu Chen Sem-Lin |
| author_facet | Bin Xu Chen Sem-Lin |
| author_sort | Bin Xu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ab0f5ca296e54824a7cc0f2a7e4066a7 |
| institution | DOAJ |
| issn | 1687-5257 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-ab0f5ca296e54824a7cc0f2a7e4066a72025-08-20T03:04:25ZengWileyJournal of Control Science and Engineering1687-52572023-01-01202310.1155/2023/3958434Motion Trajectory Error of Robotic Arm Based on Neural Network AlgorithmBin Xu0Chen Sem-Lin1School of Mechanical and Electrical EngineeringSchool of Mechanical and Electrical EngineeringIn 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.http://dx.doi.org/10.1155/2023/3958434 |
| spellingShingle | Bin Xu Chen Sem-Lin Motion Trajectory Error of Robotic Arm Based on Neural Network Algorithm Journal of Control Science and Engineering |
| title | Motion Trajectory Error of Robotic Arm Based on Neural Network Algorithm |
| title_full | Motion Trajectory Error of Robotic Arm Based on Neural Network Algorithm |
| title_fullStr | Motion Trajectory Error of Robotic Arm Based on Neural Network Algorithm |
| title_full_unstemmed | Motion Trajectory Error of Robotic Arm Based on Neural Network Algorithm |
| title_short | Motion Trajectory Error of Robotic Arm Based on Neural Network Algorithm |
| title_sort | motion trajectory error of robotic arm based on neural network algorithm |
| url | http://dx.doi.org/10.1155/2023/3958434 |
| work_keys_str_mv | AT binxu motiontrajectoryerrorofroboticarmbasedonneuralnetworkalgorithm AT chensemlin motiontrajectoryerrorofroboticarmbasedonneuralnetworkalgorithm |