State Estimation of Suspension System Based on Interacting Multiple Model Unscented Kalman Filter

doi:10.3969/j.issn.1000-1093.2021.02.003

Abstract

Abstract: The accuracy estimation of suspension state under the conditions of time-varying road excitation and model parameter uncertainty is realized to effectively solve the issue that the state estimation of the nonlinear suspension system cannot be accurately achieved under complex driving conditions. The state estimation of suspension system is studied. Based on the models of road profile excitation and nonlinear suspension system, a novel interacting multiple model unscented Kalman filter (IMMUKF) algorithm is designed using the interacting multiple model algorithm and Markovchain Monte Carlo theory. IMMUKF algorithm is used to estimate the movement state of suspension system under various working conditions. The stability conditions of the proposed algorithm is validated using the stochastic stability theory. The accuracy of the nonlinear suspension movement state was estimated in real-time by comparing the traditional unscented Kalman filter (UKF) algorithm with the proposed IMMUKF algorithm under the various road inputs, and the suspension system was tested and verified. Experimental and simulated results show that the higher accuracy of the proposed algorithm can be obtained, and the maximum root mean square error of state estimation of the proposed algorithm in simulation is less than 8%.

Key words: suspensionsystem, stateestimation, interactingmultiplemodel, unscentedKalmanfilter, Markovchainmatrix, MonteCarlo

CLC Number:

WANG Zhenfeng， LI Fei， WANG Xinyu， YANG Jiansen， QIN Yechen. State Estimation of Suspension System Based on Interacting Multiple Model Unscented Kalman Filter[J]. Acta Armamentarii, 2021, 42(2): 242-253.

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