Road Profile Estimation for Semi-Active Suspension Using an Adaptive Kalman Filter and an Adaptive Super-Twisting Observer Conference Paper uri icon

abstract

  • © 2017 American Automatic Control Council (AACC). 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.

author list (cited authors)

  • Qin, Y., Langari, R., Wang, Z., Xiang, C., & Dong, M.

citation count

  • 13

publication date

  • May 2017

publisher