Research on Tracking Control of Unmanned Mine Trucks Based on Adaptive Preview
In the view that lateral tracking accuracy of mine trucks is not high under the condition of complex path and muddy road, in order to improve the adaptability of the control algorithm to the complex driving environment in the mining area, an adaptive preview tracking control algorithm considering mu...
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| Format: | Article |
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Editorial Office of Control and Information Technology
2022-10-01
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| Series: | Kongzhi Yu Xinxi Jishu |
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| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2022.05.008 |
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| _version_ | 1849224973234733056 |
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| author | HUANG Yaoran LIU Zhicong KANG Yuanrong |
| author_facet | HUANG Yaoran LIU Zhicong KANG Yuanrong |
| author_sort | HUANG Yaoran |
| collection | DOAJ |
| description | In the view that lateral tracking accuracy of mine trucks is not high under the condition of complex path and muddy road, in order to improve the adaptability of the control algorithm to the complex driving environment in the mining area, an adaptive preview tracking control algorithm considering multi-factor fusion is proposed in this paper. Firstly, according to the vehicle kinematics, a rear wheel feedback tracking model based on preview is established. Secondly, vehicle trajectory is predicted by the trajectory prediction method. Considering the cumulative tracking error between reference trajectory and predicted trajectory, vehicle steering response delay and other factors, a multi-objective optimization function of adaptive preview is constructed. Finally, the optimization function is solved by genetic algorithms (GA), and the optimal preview point is output to the rear wheel feedback controller to realize the optimal control of the vehicle in the global path tracking process. Simulation and real vehicle test results show that when a vehicle starts with an initial lateral error of less than 0.8 m and a yaw angle error of 5°, the adaptive preview tracking control algorithm can achieve a certain degree of deviation-correcting control; the vehicle stops with a lateral error of less than 0.2 m and a yaw angle error of less than 2°, which effectively improves the tracking ability of mine trucks and its adaptability to complex working conditions. |
| format | Article |
| id | doaj-art-aaf4184554e34ef1972c45a532592e49 |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2022-10-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-aaf4184554e34ef1972c45a532592e492025-08-25T06:49:01ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272022-10-01535932310031Research on Tracking Control of Unmanned Mine Trucks Based on Adaptive PreviewHUANG YaoranLIU ZhicongKANG YuanrongIn the view that lateral tracking accuracy of mine trucks is not high under the condition of complex path and muddy road, in order to improve the adaptability of the control algorithm to the complex driving environment in the mining area, an adaptive preview tracking control algorithm considering multi-factor fusion is proposed in this paper. Firstly, according to the vehicle kinematics, a rear wheel feedback tracking model based on preview is established. Secondly, vehicle trajectory is predicted by the trajectory prediction method. Considering the cumulative tracking error between reference trajectory and predicted trajectory, vehicle steering response delay and other factors, a multi-objective optimization function of adaptive preview is constructed. Finally, the optimization function is solved by genetic algorithms (GA), and the optimal preview point is output to the rear wheel feedback controller to realize the optimal control of the vehicle in the global path tracking process. Simulation and real vehicle test results show that when a vehicle starts with an initial lateral error of less than 0.8 m and a yaw angle error of 5°, the adaptive preview tracking control algorithm can achieve a certain degree of deviation-correcting control; the vehicle stops with a lateral error of less than 0.2 m and a yaw angle error of less than 2°, which effectively improves the tracking ability of mine trucks and its adaptability to complex working conditions.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2022.05.008adaptive previewunmanned drivingpath trackingtrajectory predictiongenetic algorithmmine truck |
| spellingShingle | HUANG Yaoran LIU Zhicong KANG Yuanrong Research on Tracking Control of Unmanned Mine Trucks Based on Adaptive Preview Kongzhi Yu Xinxi Jishu adaptive preview unmanned driving path tracking trajectory prediction genetic algorithm mine truck |
| title | Research on Tracking Control of Unmanned Mine Trucks Based on Adaptive Preview |
| title_full | Research on Tracking Control of Unmanned Mine Trucks Based on Adaptive Preview |
| title_fullStr | Research on Tracking Control of Unmanned Mine Trucks Based on Adaptive Preview |
| title_full_unstemmed | Research on Tracking Control of Unmanned Mine Trucks Based on Adaptive Preview |
| title_short | Research on Tracking Control of Unmanned Mine Trucks Based on Adaptive Preview |
| title_sort | research on tracking control of unmanned mine trucks based on adaptive preview |
| topic | adaptive preview unmanned driving path tracking trajectory prediction genetic algorithm mine truck |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2022.05.008 |
| work_keys_str_mv | AT huangyaoran researchontrackingcontrolofunmannedminetrucksbasedonadaptivepreview AT liuzhicong researchontrackingcontrolofunmannedminetrucksbasedonadaptivepreview AT kangyuanrong researchontrackingcontrolofunmannedminetrucksbasedonadaptivepreview |