中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Adaptive Sliding Mode Control for Steer-by-Wire Road Vehicle Systems based on Dynamic Parameter Estimation

文献类型:会议论文

作者Ge ZK(葛志康)2,3,4; Xu ZW(许张玮)1; Wang Z(王卓)2,3,5; Bai XP(白晓平)2,3,5
出版日期2021
会议日期August 1-4, 2021
会议地点Chengdu, China
关键词adaptive sliding mode controller frequency domain estimation fast adaptation
页码171-176
英文摘要In this paper, a novel adaptive sliding mode controller (ASMC) for the steer-by-wire (SbW) system on road vehicles is proposed. It is shown that a new frequency domain identification method is able to estimate cornering stiffness effectively in the frequency domain with sinusoidal steering inputs. An adaptive sliding mode controller can then be designed reasonably. Unlike many existing sliding mode control (SMC) schemes, the proposed ASMC not only guarantees the zero-error convergence, but also has fast adaptation and strong robustness with respect to frequent changes of road conditions based on the designed estimator. Simulation results have confirmed the advantages and effectiveness of the proposed ASMC from comparing with the traditional SMC.
产权排序1
会议录Proceedings of the 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021
会议录出版者IEEE
会议录出版地New York
语种英语
ISBN号978-1-6654-2248-2
WOS记录号WOS:000709847700031
源URL[http://ir.sia.cn/handle/173321/29677]  
专题沈阳自动化研究所_数字工厂研究室
中国科学院沈阳自动化研究所
通讯作者Ge ZK(葛志康)
作者单位1.School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
4.University of Chinese Academy of Sciences, Beijing 100049, China
5.Key Laboratory of Agricultural Equipment Intelligent Technology, Liaoning Province, Shenyang 110169, China
推荐引用方式
GB/T 7714
Ge ZK,Xu ZW,Wang Z,et al. Adaptive Sliding Mode Control for Steer-by-Wire Road Vehicle Systems based on Dynamic Parameter Estimation[C]. 见:. Chengdu, China. August 1-4, 2021.

入库方式: OAI收割

来源:沈阳自动化研究所

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