@inproceedings{6300f09316194cfb9c53879b06cea1b3,
title = "Research on Sideslip Angle Estimation and Prediction for Electric Tracked Vehicle",
abstract = "Electric tracked vehicles have better maneuverability than the tracked vehicles driven by conventional powertrain. Tracked vehicles may slide laterally when turning at high speed and with large curvature. This study develops sideslip angle estimators and predictors with potential for online application based on long-short-term memory (LSTM) neural network. The estimator and the predictors with 0.1 s and 0.2 s prediction horizons can achieve good accuracy. Other predictors with longer prediction horizons have significant errors, and the root mean square and mean errors increase almost linearly with the prediction horizon. The estimated and predicted sideslip angle can be used in vehicle lateral stability control.",
keywords = "Estimator, LSTM, Sideslip angle, Tracked vehicle",
author = "Jiangtao Gai and Yue Ma and Xuzhao Hou and Gen Zeng and Shumin Ruan",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 18th Chinese Intelligent Systems Conference, CISC 2022 ; Conference date: 15-10-2022 Through 16-10-2022",
year = "2022",
doi = "10.1007/978-981-19-6226-4_57",
language = "English",
isbn = "9789811962257",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "576--583",
editor = "Yingmin Jia and Weicun Zhang and Yongling Fu and Shoujun Zhao",
booktitle = "Proceedings of 2022 Chinese Intelligent Systems Conference - Volume II",
address = "Germany",
}