Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles
This paper addresses the significant variations in model parameters observed in autonomous commercial vehicles in comparison to passenger cars, with a disparity noted largely due to changes in load. Additionally, it tackles the issue of path tracking inaccuracy caused by external factors such as del...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/953 |
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| author | Zhihong Wang Jiefeng Zhong Jie Hu Zhiling Zhang Wenlong Zhao |
| author_facet | Zhihong Wang Jiefeng Zhong Jie Hu Zhiling Zhang Wenlong Zhao |
| author_sort | Zhihong Wang |
| collection | DOAJ |
| description | This paper addresses the significant variations in model parameters observed in autonomous commercial vehicles in comparison to passenger cars, with a disparity noted largely due to changes in load. Additionally, it tackles the issue of path tracking inaccuracy caused by external factors such as delays in steering system execution. The proposed solution is a hierarchical control method, grounded in mass estimation and model predictive control(MPC). Initially, to counter the variation in model parameters, a mass estimator is developed. This estimator utilizes the recursive least squares method with a forgetting factor, coupled with M-estimation, thereby enhancing the robustness of the estimation and achieving model correction. Subsequently, an upper-level MPC controller is constructed based on the error model, thereby augmenting the precision of tracking control. To address the delay in the steering system execution common in autonomous commercial vehicles, a lower-level steering angle compensator is designed to expedite the response speed of the execution. The feasibility of the vehicle’s front wheel angle is constrained via the rollover index, thereby enhancing vehicle stability during operation. The efficacy of the proposed control strategy is demonstrated with joint simulations using TruckSim/Simulink and real vehicle tests. The results indicate that this strategy can effectively manage the model mismatch caused by load changes in commercial vehicles and the delay in steering system execution, thereby exhibiting commendable tracking accuracy, adaptability, and driving stability. |
| format | Article |
| id | doaj-art-bd3009f4a7ae4fc298c680ee4587ebc8 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-bd3009f4a7ae4fc298c680ee4587ebc82025-01-24T13:21:28ZengMDPI AGApplied Sciences2076-34172025-01-0115295310.3390/app15020953Mass Estimation-Based Path Tracking Control for Autonomous Commercial VehiclesZhihong Wang0Jiefeng Zhong1Jie Hu2Zhiling Zhang3Wenlong Zhao4Hubei Key Laboratory of Modern Auto Parts Technology, Wuhan University of Technology, Wuhan 430070, ChinaHubei Key Laboratory of Modern Auto Parts Technology, Wuhan University of Technology, Wuhan 430070, ChinaHubei Key Laboratory of Modern Auto Parts Technology, Wuhan University of Technology, Wuhan 430070, ChinaHubei Key Laboratory of Modern Auto Parts Technology, Wuhan University of Technology, Wuhan 430070, ChinaHubei Key Laboratory of Modern Auto Parts Technology, Wuhan University of Technology, Wuhan 430070, ChinaThis paper addresses the significant variations in model parameters observed in autonomous commercial vehicles in comparison to passenger cars, with a disparity noted largely due to changes in load. Additionally, it tackles the issue of path tracking inaccuracy caused by external factors such as delays in steering system execution. The proposed solution is a hierarchical control method, grounded in mass estimation and model predictive control(MPC). Initially, to counter the variation in model parameters, a mass estimator is developed. This estimator utilizes the recursive least squares method with a forgetting factor, coupled with M-estimation, thereby enhancing the robustness of the estimation and achieving model correction. Subsequently, an upper-level MPC controller is constructed based on the error model, thereby augmenting the precision of tracking control. To address the delay in the steering system execution common in autonomous commercial vehicles, a lower-level steering angle compensator is designed to expedite the response speed of the execution. The feasibility of the vehicle’s front wheel angle is constrained via the rollover index, thereby enhancing vehicle stability during operation. The efficacy of the proposed control strategy is demonstrated with joint simulations using TruckSim/Simulink and real vehicle tests. The results indicate that this strategy can effectively manage the model mismatch caused by load changes in commercial vehicles and the delay in steering system execution, thereby exhibiting commendable tracking accuracy, adaptability, and driving stability.https://www.mdpi.com/2076-3417/15/2/953autonomous commercial vehiclesmass estimatessteering compensation controllermodel predictive controllateral control |
| spellingShingle | Zhihong Wang Jiefeng Zhong Jie Hu Zhiling Zhang Wenlong Zhao Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles Applied Sciences autonomous commercial vehicles mass estimates steering compensation controller model predictive control lateral control |
| title | Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles |
| title_full | Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles |
| title_fullStr | Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles |
| title_full_unstemmed | Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles |
| title_short | Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles |
| title_sort | mass estimation based path tracking control for autonomous commercial vehicles |
| topic | autonomous commercial vehicles mass estimates steering compensation controller model predictive control lateral control |
| url | https://www.mdpi.com/2076-3417/15/2/953 |
| work_keys_str_mv | AT zhihongwang massestimationbasedpathtrackingcontrolforautonomouscommercialvehicles AT jiefengzhong massestimationbasedpathtrackingcontrolforautonomouscommercialvehicles AT jiehu massestimationbasedpathtrackingcontrolforautonomouscommercialvehicles AT zhilingzhang massestimationbasedpathtrackingcontrolforautonomouscommercialvehicles AT wenlongzhao massestimationbasedpathtrackingcontrolforautonomouscommercialvehicles |