The direct yaw-moment control based on adaptive fuzzy LQR for distributed drive electric vehicles
This paper introduced a hierarchical control strategy for direct yaw moment (DYC) to enhance the handling and stability of distributed drive electric vehicles (DDEVs) at medium to high speeds. The upper controller entailed a speed-following PI controller and an adaptive fuzzy linear quadratic regula...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2024-12-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/16878132241273524 |
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| _version_ | 1850116463499673600 |
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| author | Baohua Wang Jiacheng Zhang Yu Zhang Weilong Wang |
| author_facet | Baohua Wang Jiacheng Zhang Yu Zhang Weilong Wang |
| author_sort | Baohua Wang |
| collection | DOAJ |
| description | This paper introduced a hierarchical control strategy for direct yaw moment (DYC) to enhance the handling and stability of distributed drive electric vehicles (DDEVs) at medium to high speeds. The upper controller entailed a speed-following PI controller and an adaptive fuzzy linear quadratic regulator (AFLQR) controller, with the control objectives centered on reducing the absolute value of the sideslip angle and tracking the desired yaw rate. The proposed approach utilizes a fuzzy logic-based AFLQR controller, which could dynamically adjust the weighting parameters for sideslip angle and yaw rate in response to the vehicle speed and sideslip angle, offering better adaptability to varying driving conditions. At the lower control level, a tire-dynamic-load-based torque distribution method was applied. The control strategy’s efficacy was demonstrated through co-simulation involving CarSim and Simulink. This evaluation compared AFLQR control against non-yaw control, conventional LQR control and sliding mode control (SMC), focusing on handling and stability during sinusoidal steering wheel input test and double lane change maneuver. Results highlight that AFLQR reduces the sideslip angle by 7.88% and the yaw rate error by 84.29% compared to LQR, enhancing vehicle handling and stability. Lastly, a hardware-in-the-loop (HIL) experiment verified the control strategy’s validity. |
| format | Article |
| id | doaj-art-4e749397535c402e9bef9ed47ba12c94 |
| institution | OA Journals |
| issn | 1687-8140 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj-art-4e749397535c402e9bef9ed47ba12c942025-08-20T02:36:19ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402024-12-011610.1177/16878132241273524The direct yaw-moment control based on adaptive fuzzy LQR for distributed drive electric vehiclesBaohua Wang0Jiacheng Zhang1Yu Zhang2Weilong Wang3Hubei Longzhong Laboratory, Xiangyang, P.R. ChinaHubei Key Laboratory of Automotive Power Train and Electric Control, Shiyan, P.R. ChinaHubei Key Laboratory of Automotive Power Train and Electric Control, Shiyan, P.R. ChinaDepartment of Automotive, Hubei Hanjiang Technician College, Shiyan, P.R. ChinaThis paper introduced a hierarchical control strategy for direct yaw moment (DYC) to enhance the handling and stability of distributed drive electric vehicles (DDEVs) at medium to high speeds. The upper controller entailed a speed-following PI controller and an adaptive fuzzy linear quadratic regulator (AFLQR) controller, with the control objectives centered on reducing the absolute value of the sideslip angle and tracking the desired yaw rate. The proposed approach utilizes a fuzzy logic-based AFLQR controller, which could dynamically adjust the weighting parameters for sideslip angle and yaw rate in response to the vehicle speed and sideslip angle, offering better adaptability to varying driving conditions. At the lower control level, a tire-dynamic-load-based torque distribution method was applied. The control strategy’s efficacy was demonstrated through co-simulation involving CarSim and Simulink. This evaluation compared AFLQR control against non-yaw control, conventional LQR control and sliding mode control (SMC), focusing on handling and stability during sinusoidal steering wheel input test and double lane change maneuver. Results highlight that AFLQR reduces the sideslip angle by 7.88% and the yaw rate error by 84.29% compared to LQR, enhancing vehicle handling and stability. Lastly, a hardware-in-the-loop (HIL) experiment verified the control strategy’s validity.https://doi.org/10.1177/16878132241273524 |
| spellingShingle | Baohua Wang Jiacheng Zhang Yu Zhang Weilong Wang The direct yaw-moment control based on adaptive fuzzy LQR for distributed drive electric vehicles Advances in Mechanical Engineering |
| title | The direct yaw-moment control based on adaptive fuzzy LQR for distributed drive electric vehicles |
| title_full | The direct yaw-moment control based on adaptive fuzzy LQR for distributed drive electric vehicles |
| title_fullStr | The direct yaw-moment control based on adaptive fuzzy LQR for distributed drive electric vehicles |
| title_full_unstemmed | The direct yaw-moment control based on adaptive fuzzy LQR for distributed drive electric vehicles |
| title_short | The direct yaw-moment control based on adaptive fuzzy LQR for distributed drive electric vehicles |
| title_sort | direct yaw moment control based on adaptive fuzzy lqr for distributed drive electric vehicles |
| url | https://doi.org/10.1177/16878132241273524 |
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