TY - GEN
T1 - Fault Reconstruction Method of Neural Network Observer Group for High-Speed Vehicle
AU - Li, Cong
AU - Ding, Yibo
AU - Bi, Cheng
AU - Yue, Xiaokui
AU - Wang, Yuhao
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Aiming at the actuator failure of the attitude control system for high-speed vehicle, a fault reconstruction method of observer group based on neural network classifier is introduced. Firstly, the model of the vehicle and the failure model are established. Secondly, a dataset that represents the characteristics of step and sinusoidal fault information is established, which is used to train neural networks in order to classify fault information. Then, according to the classification results, appropriate observers related to distinct fault types for fault reconstruction are selected. For the purpose of solving the problem that different fault types have different performance requirements for observers, an observer group which contains a high-order sliding mode observer and an iterative learning observer is proposed. It can meet high accuracy requirements of step-form faults and fast response requirements of sinusoidal-form faults, so as to realize higher effective fault reconstruction. The classification fault reconstruction method can achieve excellent estimation of angular velocity and fault value. At last, the efficiency of the introduced method is verified by simulation.
AB - Aiming at the actuator failure of the attitude control system for high-speed vehicle, a fault reconstruction method of observer group based on neural network classifier is introduced. Firstly, the model of the vehicle and the failure model are established. Secondly, a dataset that represents the characteristics of step and sinusoidal fault information is established, which is used to train neural networks in order to classify fault information. Then, according to the classification results, appropriate observers related to distinct fault types for fault reconstruction are selected. For the purpose of solving the problem that different fault types have different performance requirements for observers, an observer group which contains a high-order sliding mode observer and an iterative learning observer is proposed. It can meet high accuracy requirements of step-form faults and fast response requirements of sinusoidal-form faults, so as to realize higher effective fault reconstruction. The classification fault reconstruction method can achieve excellent estimation of angular velocity and fault value. At last, the efficiency of the introduced method is verified by simulation.
KW - Fault Reconstruction
KW - High-order Sliding Mode Observer
KW - High-speed vehicle
KW - Iterative Learning Observer
KW - Neural Networks
UR - http://www.scopus.com/inward/record.url?scp=105006483673&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-2228-3_27
DO - 10.1007/978-981-96-2228-3_27
M3 - 会议稿件
AN - SCOPUS:105006483673
SN - 9789819622276
T3 - Lecture Notes in Electrical Engineering
SP - 292
EP - 301
BT - Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 8
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Deng, Yimin
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2024
Y2 - 9 August 2024 through 11 August 2024
ER -