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

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Abstract

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.

Original languageEnglish
Title of host publication2017 American Control Conference, ACC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages973-978
Number of pages6
ISBN (Electronic)9781509059928
DOIs
Publication statusPublished - 29 Jun 2017
Event2017 American Control Conference, ACC 2017 - Seattle, United States
Duration: 24 May 201726 May 2017

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Conference

Conference2017 American Control Conference, ACC 2017
Country/TerritoryUnited States
CitySeattle
Period24/05/1726/05/17

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