Sliding Mode Backstepping Control of Excavator Bucket Trajectory Synovial in Particle Swarm Optimization Algorithm and Neural Network Disturbance Observer
As a representative of multi-functional engineering machinery, the excavator is irreplaceable in the field of engineering construction. To autonomously control the excavator bucket, it is essential to control the position of the bucket hydraulic cylinder. As a consequence of the problem of position...
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2025-01-01
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| author | Xiangfei Tao Kailei Liu Jing Yang Yu Chen Jiayuan Chen Haoran Zhu |
| author_facet | Xiangfei Tao Kailei Liu Jing Yang Yu Chen Jiayuan Chen Haoran Zhu |
| author_sort | Xiangfei Tao |
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| description | As a representative of multi-functional engineering machinery, the excavator is irreplaceable in the field of engineering construction. To autonomously control the excavator bucket, it is essential to control the position of the bucket hydraulic cylinder. As a consequence of the problem of position tracking control spawned from external disturbance and other factors in the self-mining servo system of excavators, a strategy of sliding mode backstepping control based on the particle swarm optimization algorithm and neural network disturbance observer (PSO-NNDO-SMBC) was recommended accordingly. Meanwhile, the complex disturbance was estimated online and compensated for by the system control input by the universal approximation property of the neural network disturbance observer (NNDO). Afterwards, the uncertainty of control parameters was optimized by the particle swarm optimization algorithm (PSO) and was fed back to the controller parameter input end. Afterwards, a co-simulation model of MATLAB/Simulink (MATLAB2023b) and AMESim (Simcenter Amesim 2304) was established for simulation analysis, and a test bench was set up for comparison and verification. As proven by the experimental results, PSO-NNDO-SMBC possessed strong anti-interference ability. In contrast to the sliding mode backstepping control based on the particle swarm optimization algorithm (PSO-SMBC), the maximum displacement tracking error was lowered by 50.5%. Furthermore, in comparison with the Proportional-Integral-Derivative (PID), the maximum displacement tracking error was decreased by 75.2%, which tremendously optimized the control accuracy of excavator bucket displacement tracking. |
| format | Article |
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| institution | Kabale University |
| issn | 2076-0825 |
| language | English |
| publishDate | 2025-01-01 |
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| series | Actuators |
| spelling | doaj-art-e9eacee5de4e4cafb26e2f8c4f7524702025-01-24T13:15:09ZengMDPI AGActuators2076-08252025-01-01141910.3390/act14010009Sliding Mode Backstepping Control of Excavator Bucket Trajectory Synovial in Particle Swarm Optimization Algorithm and Neural Network Disturbance ObserverXiangfei Tao0Kailei Liu1Jing Yang2Yu Chen3Jiayuan Chen4Haoran Zhu5School of Mechanical Engineering, Jiangsu University of Technology, Changzhou 213001, ChinaSchool of Mechanical Engineering, Jiangsu University of Technology, Changzhou 213001, ChinaSchool of Mechanical Engineering, Jiangsu University of Technology, Changzhou 213001, ChinaSchool of Mechanical Engineering, Jiangsu University of Technology, Changzhou 213001, ChinaSchool of Mechanical Engineering, Jiangsu University of Technology, Changzhou 213001, ChinaSchool of Mechanical Engineering, Jiangsu University of Technology, Changzhou 213001, ChinaAs a representative of multi-functional engineering machinery, the excavator is irreplaceable in the field of engineering construction. To autonomously control the excavator bucket, it is essential to control the position of the bucket hydraulic cylinder. As a consequence of the problem of position tracking control spawned from external disturbance and other factors in the self-mining servo system of excavators, a strategy of sliding mode backstepping control based on the particle swarm optimization algorithm and neural network disturbance observer (PSO-NNDO-SMBC) was recommended accordingly. Meanwhile, the complex disturbance was estimated online and compensated for by the system control input by the universal approximation property of the neural network disturbance observer (NNDO). Afterwards, the uncertainty of control parameters was optimized by the particle swarm optimization algorithm (PSO) and was fed back to the controller parameter input end. Afterwards, a co-simulation model of MATLAB/Simulink (MATLAB2023b) and AMESim (Simcenter Amesim 2304) was established for simulation analysis, and a test bench was set up for comparison and verification. As proven by the experimental results, PSO-NNDO-SMBC possessed strong anti-interference ability. In contrast to the sliding mode backstepping control based on the particle swarm optimization algorithm (PSO-SMBC), the maximum displacement tracking error was lowered by 50.5%. Furthermore, in comparison with the Proportional-Integral-Derivative (PID), the maximum displacement tracking error was decreased by 75.2%, which tremendously optimized the control accuracy of excavator bucket displacement tracking.https://www.mdpi.com/2076-0825/14/1/9excavator bucketelectro-hydraulic servo systemparticle swarm optimizationneural network disturbance observersliding mode backstepping control |
| spellingShingle | Xiangfei Tao Kailei Liu Jing Yang Yu Chen Jiayuan Chen Haoran Zhu Sliding Mode Backstepping Control of Excavator Bucket Trajectory Synovial in Particle Swarm Optimization Algorithm and Neural Network Disturbance Observer Actuators excavator bucket electro-hydraulic servo system particle swarm optimization neural network disturbance observer sliding mode backstepping control |
| title | Sliding Mode Backstepping Control of Excavator Bucket Trajectory Synovial in Particle Swarm Optimization Algorithm and Neural Network Disturbance Observer |
| title_full | Sliding Mode Backstepping Control of Excavator Bucket Trajectory Synovial in Particle Swarm Optimization Algorithm and Neural Network Disturbance Observer |
| title_fullStr | Sliding Mode Backstepping Control of Excavator Bucket Trajectory Synovial in Particle Swarm Optimization Algorithm and Neural Network Disturbance Observer |
| title_full_unstemmed | Sliding Mode Backstepping Control of Excavator Bucket Trajectory Synovial in Particle Swarm Optimization Algorithm and Neural Network Disturbance Observer |
| title_short | Sliding Mode Backstepping Control of Excavator Bucket Trajectory Synovial in Particle Swarm Optimization Algorithm and Neural Network Disturbance Observer |
| title_sort | sliding mode backstepping control of excavator bucket trajectory synovial in particle swarm optimization algorithm and neural network disturbance observer |
| topic | excavator bucket electro-hydraulic servo system particle swarm optimization neural network disturbance observer sliding mode backstepping control |
| url | https://www.mdpi.com/2076-0825/14/1/9 |
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