Robust Sliding Mode-Based Learning Control for Lane-Keeping Systems in Autonomous Vehicles
文献类型：会议论文
| 作者 | Ge ZK(葛志康)1,2,4 ; Wang Z(王卓)1,3,4; Bai XP(白晓平)1,3,4; Wang XX(王枭雄)1,2,4
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| 出版日期 | 2021 |
| 会议日期 | August 1-4, 2021 |
| 会议地点 | Chengdu, China |
| 关键词 | sliding mode-based learning control (SMLC) Lipschitz-like condition strong robustness |
| 页码 | 1856-1861 |
| 英文摘要 | In this paper, a robust sliding mode-based learning control (SMLC) scheme for lane-keeping systems (LKS) of road vehicles is proposed. It is assumed that all of signals in system satisfy Lipschitz-like condition, a robust sliding mode-based learning controller is designed to achieve the zero-error convergence of lateral position error dynamics. A new finding is that yaw angle error dynamics is able to converge to zero asymptotically on the sliding surface. Unlike many existing sliding mode control schemes, the proposed SMLC scheme does not require the bound information of unknown system parameters. More significantly, the LKS equipped with the SMLC algorithm exhibits a strong robustness against varying road conditions and external disturbances. Simulation results demonstrate that the designed SMLC scheme could exert excellent tracking performance and robustness. |
| 产权排序 | 1 |
| 会议录 | Proceedings of the 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021
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| 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:000709847700330 |
| 源URL | [http://ir.sia.cn/handle/173321/29676]  |
| 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.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 2.University of Chinese Academy of Sciences, Beijing 100049, China 3.Key Laboratory of Agricultural Equipment Intelligent Technology, Liaoning Province, Shenyang 110169, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China |
| 推荐引用方式 GB/T 7714 | Ge ZK,Wang Z,Bai XP,et al. Robust Sliding Mode-Based Learning Control for Lane-Keeping Systems in Autonomous Vehicles[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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