RHONN modelling-enabled nonlinear predictive control for lateral dynamics stabilization of an in-wheel motor driven vehicle

Featuring the fast response and flexibility in control allocation, an electric vehicle with in-wheel motors is a good platform for implementing advanced vehicle dynamics control. Among many active safety functions of an in-wheel motor driven vehicle (IMDV), lateral stability control is a key technol...

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Main Authors: Chen, Hao, Zhang, Junzhi, Lv, Chen
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/163837
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1638372022-12-19T07:28:31Z RHONN modelling-enabled nonlinear predictive control for lateral dynamics stabilization of an in-wheel motor driven vehicle Chen, Hao Zhang, Junzhi Lv, Chen School of Mechanical and Aerospace Engineering Engineering::Mechanical engineering Lateral Stability Control Nonlinear Model Predictive Control Featuring the fast response and flexibility in control allocation, an electric vehicle with in-wheel motors is a good platform for implementing advanced vehicle dynamics control. Among many active safety functions of an in-wheel motor driven vehicle (IMDV), lateral stability control is a key technology, which can be realized through torque vectoring. To further advance the lateral stabilization performance of the IMDV, in this article a novel data-driven nonlinear model predictive control (NMPC) is proposed based the recurrent high-order neural network (RHONN) modelling method. First, the new RHONN model is developed to represent vehicle's nonlinear dynamic behaviors. Different from the conventional physics-based modelling method, the RHONN model forms high-order polynomials by neuron states to feature nonlinear dynamics. Based on the RHONN model, the steady-state responses of vehicle's yaw rate and sideslip angle are iteratively optimized and set as the control objectives for low-level controller, aiming to improve the system robustness. Besides, a nonlinear model predictive controller is designed based on the RHONN, which is expected to improve the prediction accuracy during the receding horizon control. Further, a constrained optimization problem is formulated to derive the required yaw moment for vehicle lateral dynamics stabilization. Finally, the performance of the developed RHONN-based nonlinear MPC is validated on an IMDV in the CarSim/Simulink simulation environment. The validation results show that the developed approach outperforms the conventional method, and further improves the stable margin of the system. It is able to enhance the lateral stabilization performance of the IMDV under various driving scenarios, demonstrating the feasibility and effectiveness of the proposed approach. Agency for Science, Technology and Research (A*STAR) Nanyang Technological University This work was supported in part by the Agency for Science, Technology and Research (A*STAR) under AME Young Individual Research Grant A2084c0156, in part by IAF-ICP Programme under Grant ICP1900093, and in part by the Schaeffler Hub for Advanced Research at NTU. 2022-12-19T07:28:31Z 2022-12-19T07:28:31Z 2022 Journal Article Chen, H., Zhang, J. & Lv, C. (2022). RHONN modelling-enabled nonlinear predictive control for lateral dynamics stabilization of an in-wheel motor driven vehicle. IEEE Transactions On Vehicular Technology, 71(8), 8296-8308. https://dx.doi.org/10.1109/TVT.2022.3172870 0018-9545 https://hdl.handle.net/10356/163837 10.1109/TVT.2022.3172870 2-s2.0-85132529792 8 71 8296 8308 en A2084c0156 ICP1900093 IEEE Transactions on Vehicular Technology © 2022 IEEE. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Mechanical engineering
Lateral Stability Control
Nonlinear Model Predictive Control
spellingShingle Engineering::Mechanical engineering
Lateral Stability Control
Nonlinear Model Predictive Control
Chen, Hao
Zhang, Junzhi
Lv, Chen
RHONN modelling-enabled nonlinear predictive control for lateral dynamics stabilization of an in-wheel motor driven vehicle
description Featuring the fast response and flexibility in control allocation, an electric vehicle with in-wheel motors is a good platform for implementing advanced vehicle dynamics control. Among many active safety functions of an in-wheel motor driven vehicle (IMDV), lateral stability control is a key technology, which can be realized through torque vectoring. To further advance the lateral stabilization performance of the IMDV, in this article a novel data-driven nonlinear model predictive control (NMPC) is proposed based the recurrent high-order neural network (RHONN) modelling method. First, the new RHONN model is developed to represent vehicle's nonlinear dynamic behaviors. Different from the conventional physics-based modelling method, the RHONN model forms high-order polynomials by neuron states to feature nonlinear dynamics. Based on the RHONN model, the steady-state responses of vehicle's yaw rate and sideslip angle are iteratively optimized and set as the control objectives for low-level controller, aiming to improve the system robustness. Besides, a nonlinear model predictive controller is designed based on the RHONN, which is expected to improve the prediction accuracy during the receding horizon control. Further, a constrained optimization problem is formulated to derive the required yaw moment for vehicle lateral dynamics stabilization. Finally, the performance of the developed RHONN-based nonlinear MPC is validated on an IMDV in the CarSim/Simulink simulation environment. The validation results show that the developed approach outperforms the conventional method, and further improves the stable margin of the system. It is able to enhance the lateral stabilization performance of the IMDV under various driving scenarios, demonstrating the feasibility and effectiveness of the proposed approach.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Chen, Hao
Zhang, Junzhi
Lv, Chen
format Article
author Chen, Hao
Zhang, Junzhi
Lv, Chen
author_sort Chen, Hao
title RHONN modelling-enabled nonlinear predictive control for lateral dynamics stabilization of an in-wheel motor driven vehicle
title_short RHONN modelling-enabled nonlinear predictive control for lateral dynamics stabilization of an in-wheel motor driven vehicle
title_full RHONN modelling-enabled nonlinear predictive control for lateral dynamics stabilization of an in-wheel motor driven vehicle
title_fullStr RHONN modelling-enabled nonlinear predictive control for lateral dynamics stabilization of an in-wheel motor driven vehicle
title_full_unstemmed RHONN modelling-enabled nonlinear predictive control for lateral dynamics stabilization of an in-wheel motor driven vehicle
title_sort rhonn modelling-enabled nonlinear predictive control for lateral dynamics stabilization of an in-wheel motor driven vehicle
publishDate 2022
url https://hdl.handle.net/10356/163837
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