Evidence map›Paper›PMID 36560162›Full record

ArticleSensors (Basel, Switzerland)2022

Vehicle Stability Analysis under Extreme Operating Conditions Based on LQR Control.

Liping Wu, Ran Zhou, Junshan Bao, Guang Yang, Feng Sun, Fangchao Xu, Junjie Jin, Qi Zhang, Weikang Jiang, Xiaoyou Zhang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

10 authors.

Liping WuSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China.
Ran ZhouSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China.ORCID 0000-0003-4390-0589
Junshan BaoSIASUN Robot & Automation Co., Ltd., Shenyang 110169, China.
Guang YangSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China.
Feng SunSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China.
Fangchao XuSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China.
Junjie JinSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China.
Qi ZhangSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China.
Weikang JiangDepartment of Mechanical Engineering, Nippon Institute of Technology, Saitama 345-8501, Japan.
Xiaoyou ZhangSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China.

Funding

Major Project of the Ministry of Science and Technology of Liaoning Province 2022JH1/10400027National Key Research and Development Project 2020YFC2006701National Natural Science Fund of China 52005344National Natural Science Fund of China 52005345Scientific Research Fund Project of Liaoning Provincial Department of Education LFGD2020002
6 · The paper itself

Abstract

Under extreme working conditions such as high-speed driving on roads with a large road surface unevenness coefficient, turning on a road with a low road surface adhesion coefficient, and emergency acceleration and braking, a vehicle's stability deteriorates sharply and reduces ride comfort. There is extensive existing research on vehicle active suspension control, trajectory tracking, and control methods. However, most of these studies focus on conventional operating conditions, while vehicle stability analysis under extreme operating conditions is much less studied. In order to improve the stability of the whole vehicle under extreme operating conditions, this paper investigates the stability of a vehicle under extreme operating conditions based on linear quadratic regulator (LQR) control. First, a seven degrees of freedom (7-DOF) dynamics model of the whole vehicle is established based on the use of electromagnetic active suspension, and then an LQR controller of the electromagnetic active suspension is designed. A joint simulation platform incorporating MATLAB and CarSim was built, and the CarSim model is verified by real vehicle tests. Finally, the stability of the vehicle under four different ultimate operating conditions was analyzed. The simulation results show that the root mean square (RMS) values of body droop acceleration and pitch angle acceleration are improved by 57.48% and 28.81%, respectively, under high-speed driving conditions on Class C roads. Under the double-shift condition with a low adhesion coefficient, the RMS values of body droop acceleration, pitch acceleration, and roll angle acceleration are improved by 58.25%, 55.41%, and 31.39%, respectively. These results indicate that electromagnetic active suspension can significantly improve vehicle stability and reduce driving risk under extreme working conditions when combined with an LQR controller.

Indexed as

CarSimextreme operating conditionsLQR controllervehicle stability

Identifiers

PMID36560162
PMCPMC9783117

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