期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 144, 期 -, 页码 -出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2020.106862
关键词
Sideslip angle estimation; Vehicle dynamics; Kalman filters; Deep ensemble; Uncertainty
资金
- Technology Innovation Program (Industrial Strategic technology development program) [20006862]
- Ministry of Trade, Industry & Energy (MOTIE, Korea)
This paper presents a novel sideslip angle estimation scheme combining deep neural network and nonlinear Kalman filters. The deep neural network contains a recurrent neural network with long short-term memory which is effective for analyzing sequential sensor data and deep ensemble which is used for robustness of the estimation and acquisition of the uncertainty of the estimate. The deep neural network is trained using input sets which consist of on-board sensor measurements (yawrate, velocity, steering wheel angle and lateral acceleration) and provides sideslip angle estimate and its uncertainty. The estimate of deep neural network is used as a new measure in the nonlinear Kalman filters and its uncertainty is used to make an adaptive measurement covariance matrix. The algorithm is verified through both simulation and experiment. The performance with the proposed method is analyzed in terms of the root mean squared error (RMSE) and maximum error (ME) as compared to the case where nonlinear Kalman filter or deep neural network is utilized individually. The results demonstrate the effectiveness of the proposed solution. (C) 2020 Elsevier Ltd. All rights reserved.
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