Adaptive Unscented Kalman Filter Algorithm for Identifying and Analyzing the Dynamic Response Model Parameters of StrandedWire Helical Springs

doi:10.3969/j.issn.1000-1093.2018.01.003

Abstract

Abstract: An adaptive unscented Kalman filter (AUKF) algorithm with noise statistic estimator is developed for the parameters identification of nonlinear response model of stranded wire helical springs. The convergence of parameters identification of nonlinear model could be ensured by recursively estimating measurement noise (or process noise) in the test data of stranded wire helical springs. The effectiveness of AUKF algorithm is verified via dynamic loading experiment. The result demonstrates that the proposed algorithm can accurately determine the model parameters of stranded wire helical springs even with higher levels of measurement noise. In the prediction process of spring response, the predicted amplitude should not be much smaller than the amplitude for parameter identification. When the loading rate is changed, the hysteresis parameters in the dynamic model of stranded wire helical spring are basically unchanged, but the zero-order nonlinear stiffness coefficient and the zero-order nonlinear amplification factor are changed greatly. Key

Key words: strandedwirehelicalspring, parameteridentification, nonlinearhysteresismodel, adaptiveunscentedKalmanfilteralgorithm

CLC Number:

TJ203⁺.6

DING Chuan-jun, ZHANG Xiang-yan, LIU Ning. Adaptive Unscented Kalman Filter Algorithm for Identifying and Analyzing the Dynamic Response Model Parameters of StrandedWire Helical Springs[J]. Acta Armamentarii, 2018, 39(1): 28-37.

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第39卷
第1期2018 年1月兵工学报ACTA
ARMAMENTARIIVol.39No.1Jan.2018

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