Agricultural machinery photoelectric automatic navigation control system based on back propagation neural network
So as to study the influence of speed factors on the stability of tractor automatic navigation system, combined with neural network control theory, the author proposed a dual-objective joint sliding mode control method based on lateral position deviation and heading angle deviation, using back prop...
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| Format: | Article |
| Language: | English |
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PAGEPress Publications
2023-07-01
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| Series: | Journal of Agricultural Engineering |
| Subjects: | |
| Online Access: | https://agroengineering.org/index.php/jae/article/view/1530 |
| _version_ | 1827907667544244224 |
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| author | Yerong Sun Kechuan Yi |
| author_facet | Yerong Sun Kechuan Yi |
| author_sort | Yerong Sun |
| collection | DOAJ |
| description |
So as to study the influence of speed factors on the stability of tractor automatic navigation system, combined with neural network control theory, the author proposed a dual-objective joint sliding mode control method based on lateral position deviation and heading angle deviation, using back propagation neural network to establish two-wheel tractor-path dynamics model and straight-line path tracking deviation model, the overall system simulation was carried out by using Matlab/Simulink, and the reliability of the control method was verified. The experimental results showed: when the tractor was tracked with the automatic control of linear path under the condition of the variable speed, the maximum deviation of the lateral position deviation was 12.7cm, and the average absolute deviation was kept within 4.88cm; the maximum deviation of the heading angle deviation was 5°, and the average absolute deviation was kept within 2°; the maximum value of the actual rotation angle was 3.13°, and the standard deviation of the fluctuation was within 0.84°. Under the condition of constant speed and variable speed, using the joint sliding mode control method designed by the author, the dual-objective joint control of lateral position deviation and heading angle deviation could be realized, the controlled overshoot was small, the controlled deviation was small after reaching a stable state, and the adaptability to speed factors was strong, which basically could meet the accuracy requirements of farmland operations.
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| first_indexed | 2024-03-13T01:11:18Z |
| format | Article |
| id | doaj.art-024976e107664cfe9a8518ef6d6d7989 |
| institution | Directory Open Access Journal |
| issn | 1974-7071 2239-6268 |
| language | English |
| last_indexed | 2024-03-13T01:11:18Z |
| publishDate | 2023-07-01 |
| publisher | PAGEPress Publications |
| record_format | Article |
| series | Journal of Agricultural Engineering |
| spelling | doaj.art-024976e107664cfe9a8518ef6d6d79892023-07-05T19:15:05ZengPAGEPress PublicationsJournal of Agricultural Engineering1974-70712239-62682023-07-0110.4081/jae.2023.1530Agricultural machinery photoelectric automatic navigation control system based on back propagation neural networkYerong Sun0Kechuan Yi1School of Mechanical Engineering, Anhui Science and Technology University, Fengyang, AnhuiSchool of Mechanical Engineering, Anhui Science and Technology University, Fengyang, Anhui So as to study the influence of speed factors on the stability of tractor automatic navigation system, combined with neural network control theory, the author proposed a dual-objective joint sliding mode control method based on lateral position deviation and heading angle deviation, using back propagation neural network to establish two-wheel tractor-path dynamics model and straight-line path tracking deviation model, the overall system simulation was carried out by using Matlab/Simulink, and the reliability of the control method was verified. The experimental results showed: when the tractor was tracked with the automatic control of linear path under the condition of the variable speed, the maximum deviation of the lateral position deviation was 12.7cm, and the average absolute deviation was kept within 4.88cm; the maximum deviation of the heading angle deviation was 5°, and the average absolute deviation was kept within 2°; the maximum value of the actual rotation angle was 3.13°, and the standard deviation of the fluctuation was within 0.84°. Under the condition of constant speed and variable speed, using the joint sliding mode control method designed by the author, the dual-objective joint control of lateral position deviation and heading angle deviation could be realized, the controlled overshoot was small, the controlled deviation was small after reaching a stable state, and the adaptability to speed factors was strong, which basically could meet the accuracy requirements of farmland operations. https://agroengineering.org/index.php/jae/article/view/1530Back propagation neural networkautomatic navigation systemsliding mode control methodspeed adaptationsimulation analysis |
| spellingShingle | Yerong Sun Kechuan Yi Agricultural machinery photoelectric automatic navigation control system based on back propagation neural network Journal of Agricultural Engineering Back propagation neural network automatic navigation system sliding mode control method speed adaptation simulation analysis |
| title | Agricultural machinery photoelectric automatic navigation control system based on back propagation neural network |
| title_full | Agricultural machinery photoelectric automatic navigation control system based on back propagation neural network |
| title_fullStr | Agricultural machinery photoelectric automatic navigation control system based on back propagation neural network |
| title_full_unstemmed | Agricultural machinery photoelectric automatic navigation control system based on back propagation neural network |
| title_short | Agricultural machinery photoelectric automatic navigation control system based on back propagation neural network |
| title_sort | agricultural machinery photoelectric automatic navigation control system based on back propagation neural network |
| topic | Back propagation neural network automatic navigation system sliding mode control method speed adaptation simulation analysis |
| url | https://agroengineering.org/index.php/jae/article/view/1530 |
| work_keys_str_mv | AT yerongsun agriculturalmachineryphotoelectricautomaticnavigationcontrolsystembasedonbackpropagationneuralnetwork AT kechuanyi agriculturalmachineryphotoelectricautomaticnavigationcontrolsystembasedonbackpropagationneuralnetwork |