Adaptive Model Prediction of Unmanned Agricultural Machinery for Tracking Control in Mountain Environment
In order to improve the trajectory tracking accuracy and body stability of unmanned agricultural machinery in mountainous environment, this paper designs the adaptive forgetting factor related to the driving state of the agricultural machinery, and then corrects the tire turning stiffness in real ti...
| Váldodahkkit: | , , |
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| Materiálatiipa: | Artihkal |
| Giella: | English |
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IEEE
2024-01-01
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| Ráidu: | IEEE Access |
| Fáttát: | |
| Liŋkkat: | https://ieeexplore.ieee.org/document/10677016/ |
| _version_ | 1827106157499514880 |
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| author | Fuchao Liu Wenjue Chen Hailin Zhao |
| author_facet | Fuchao Liu Wenjue Chen Hailin Zhao |
| author_sort | Fuchao Liu |
| collection | DOAJ |
| description | In order to improve the trajectory tracking accuracy and body stability of unmanned agricultural machinery in mountainous environment, this paper designs the adaptive forgetting factor related to the driving state of the agricultural machinery, and then corrects the tire turning stiffness in real time based on the Adaptive Forgetting Factor Recursive Least Squares (AFFRLS) algorithm. and adaptively adjust the weight coefficients in the MPC according to the road surface attachment coefficient to achieve the dynamic control between the trajectory tracking accuracy and the lateral stability of the body of the unmanned agricultural machinery in the mountainous environment. The results show that the trajectory tracking accuracy and lateral stability of unmanned agricultural machinery in mountainous environment can be dynamically controlled compared with the previous methods. The results show that the proposed adaptive variable-parameter MPC algorithm (AMPC) control algorithm improves the tracking accuracy and stability compared to previous trajectory tracking control algorithms, resulting in a reduction of 36.1% and 26% in the peak beta and yaw rate of the unmanned agricultural machinery, respectively, and a reduction of 67% in the peak lateral tracking error. |
| first_indexed | 2025-03-20T09:50:15Z |
| format | Article |
| id | doaj.art-4a3d7eb8b2d94f929368f00e8cf924b5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2025-03-20T09:50:15Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4a3d7eb8b2d94f929368f00e8cf924b52024-09-24T23:00:23ZengIEEEIEEE Access2169-35362024-01-011213217513218510.1109/ACCESS.2024.345844210677016Adaptive Model Prediction of Unmanned Agricultural Machinery for Tracking Control in Mountain EnvironmentFuchao Liu0Wenjue Chen1https://orcid.org/0000-0003-3390-632XHailin Zhao2School of Automation, Beijing Information Science and Technology University, Beijing, ChinaSchool of Automation, Beijing Information Science and Technology University, Beijing, ChinaSchool of Automation, Beijing Information Science and Technology University, Beijing, ChinaIn order to improve the trajectory tracking accuracy and body stability of unmanned agricultural machinery in mountainous environment, this paper designs the adaptive forgetting factor related to the driving state of the agricultural machinery, and then corrects the tire turning stiffness in real time based on the Adaptive Forgetting Factor Recursive Least Squares (AFFRLS) algorithm. and adaptively adjust the weight coefficients in the MPC according to the road surface attachment coefficient to achieve the dynamic control between the trajectory tracking accuracy and the lateral stability of the body of the unmanned agricultural machinery in the mountainous environment. The results show that the trajectory tracking accuracy and lateral stability of unmanned agricultural machinery in mountainous environment can be dynamically controlled compared with the previous methods. The results show that the proposed adaptive variable-parameter MPC algorithm (AMPC) control algorithm improves the tracking accuracy and stability compared to previous trajectory tracking control algorithms, resulting in a reduction of 36.1% and 26% in the peak beta and yaw rate of the unmanned agricultural machinery, respectively, and a reduction of 67% in the peak lateral tracking error.https://ieeexplore.ieee.org/document/10677016/Unmanned agricultural machinerytire cornering stiffness estimationAFFRLStire slip angle constraintsadaptive weight coefficient |
| spellingShingle | Fuchao Liu Wenjue Chen Hailin Zhao Adaptive Model Prediction of Unmanned Agricultural Machinery for Tracking Control in Mountain Environment IEEE Access Unmanned agricultural machinery tire cornering stiffness estimation AFFRLS tire slip angle constraints adaptive weight coefficient |
| title | Adaptive Model Prediction of Unmanned Agricultural Machinery for Tracking Control in Mountain Environment |
| title_full | Adaptive Model Prediction of Unmanned Agricultural Machinery for Tracking Control in Mountain Environment |
| title_fullStr | Adaptive Model Prediction of Unmanned Agricultural Machinery for Tracking Control in Mountain Environment |
| title_full_unstemmed | Adaptive Model Prediction of Unmanned Agricultural Machinery for Tracking Control in Mountain Environment |
| title_short | Adaptive Model Prediction of Unmanned Agricultural Machinery for Tracking Control in Mountain Environment |
| title_sort | adaptive model prediction of unmanned agricultural machinery for tracking control in mountain environment |
| topic | Unmanned agricultural machinery tire cornering stiffness estimation AFFRLS tire slip angle constraints adaptive weight coefficient |
| url | https://ieeexplore.ieee.org/document/10677016/ |
| work_keys_str_mv | AT fuchaoliu adaptivemodelpredictionofunmannedagriculturalmachineryfortrackingcontrolinmountainenvironment AT wenjuechen adaptivemodelpredictionofunmannedagriculturalmachineryfortrackingcontrolinmountainenvironment AT hailinzhao adaptivemodelpredictionofunmannedagriculturalmachineryfortrackingcontrolinmountainenvironment |