期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 50, 期 11, 页码 4326-4338出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2018.2850367
关键词
Delays; Recurrent neural networks; Kalman filters; Brain modeling; Vehicle dynamics; Computational modeling; Electric vehicles; Deep learning (DL); four-wheel independently actuated (FWIA) autonomous electric vehicles; network-induced delays; recurrent neural networks (RNNs); unscented Kalman predictor (UKP)
资金
- National Natural Science Foundation of China [U1664258, 51575103]
- National Key Research and Development Program in China [2016YFB0100906, 2016YFD0700905]
- Six Talent Peaks Project in Jiangsu Province [2014-JXQC001]
- Qing Lan Project
- Fundamental Research Funds for the Central Universities [2242016K41056]
Accurate knowledge of the vehicle states is the foundation of vehicle motion control. However, in real implementations, sensory signals are always corrupted by delays and noises. Network induced time-varying delays and measurement noises can be a hazard in the active safety of over-actuated electric vehicles (EVs). In this paper, a brain-inspired proprioceptive system based on state-of-the-art deep learning and data fusion technique is proposed to solve this problem in autonomous four-wheel actuated EVs. A deep recurrent neural network (RNN) is trained by the noisy and delayed measurement signals to make accurate predictions of the vehicle motion states. Then unscented Kalman predictor, which is the adaption of unscented Kalman filter in time-varying-delay situations, combines the predictions of the RNN and corrupted sensory signals to provide better perceptions of the locomotion. Simulations with a high-fidelity, CarSim, full-vehicle model are carried out to show the effectiveness of our RNN framework and the entire proprioceptive system.
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