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Road profile estimation for semi-active suspension using an adaptive Kalman filter and an adaptive super-twisting observer

  • Beijing Institute of Technology
  • Texas AandM University

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

A novel road estimation method using an adaptive Kalman filter and an adaptive super-twisting observer (AKF-ASTO) is presented, which can meet the requirements for road excitation information of advanced suspension system. A Kalman filter is utilized to estimate the velocity of unsprung mass and control force, and the covariance matrixes of both process noise and measurement noise are adaptively tuned by a novel road classifier. The estimated variable and control force are then processed by an adaptive super-twisting observer to reconstruct the road profile and the convergence of the ASTO is ensured by a Lyapunov analysis. Simulation results for a quarter vehicle model show that AKF-ASTO can estimate both the road profile and the system states with higher accuracy compared to the existing method. The proposed method can be used for the varying International Standardization Organization (ISO) road levels, solely requiring the measurement of the accelerations of the sprung and unsprung masses.

源语言英语
主期刊名2017 American Control Conference, ACC 2017
出版商Institute of Electrical and Electronics Engineers Inc.
973-978
页数6
ISBN(电子版)9781509059928
DOI
出版状态已出版 - 29 6月 2017
已对外发布
活动2017 American Control Conference, ACC 2017 - Seattle, 美国
期限: 24 5月 201726 5月 2017

丛书

姓名Proceedings of the American Control Conference
ISSN(印刷版)0743-1619

会议

会议2017 American Control Conference, ACC 2017
国家/地区美国
Seattle
时期24/05/1726/05/17

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