Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163837
Title: RHONN modelling-enabled nonlinear predictive control for lateral dynamics stabilization of an in-wheel motor driven vehicle
Authors: Chen, Hao
Zhang, Junzhi
Lv, Chen
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: 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
Project: A2084c0156
ICP1900093 
Journal: IEEE Transactions on Vehicular Technology
Abstract: 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.
URI: https://hdl.handle.net/10356/163837
ISSN: 0018-9545
DOI: 10.1109/TVT.2022.3172870
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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