caa a22 4500 606158995 CHVBK 20210128100622.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00521-014-1705-y doi (NATIONALLICENCE)springer-10.1007/s00521-014-1705-y Adaptive fault-tolerant automatic train operation using RBF neural networks [Elektronische Daten] [Shigen Gao, Hairong Dong, Bin Ning, Yao Chen, Xubin Sun] In order to accommodate actuator failures which are unknown in amplitude and time, adaptive fault-tolerant control schemes are proposed for automatic train operation system. Firstly a basic design scheme on the basis of direct adaptive control is considered. It is demonstrated that, when actuator failures occur, asymptotical speed and position tracking are guaranteed. Then a new user-friendly control scheme is proposed which can eliminate the undesirable chattering phenomenon, which is the defect of the previous method. Simulation results verify the effectiveness of established theoretical results that satisfactory speed tracking and position tracking can be guaranteed in the presence of uncertain actuator failures in automatic train operation systems. The Natural Computing Applications Forum, 2014 Intelligent transportation system nationallicence Automatic train operation nationallicence Fault-tolerant control nationallicence RBF neural network nationallicence Gao Shigen State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, 100044, Beijing, China aut Dong Hairong State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, 100044, Beijing, China aut Ning Bin State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, 100044, Beijing, China aut Chen Yao School of Electronic and Information Engineering, Beijing Jiaotong University, 100044, Beijing, China aut Sun Xubin School of Electronic and Information Engineering, Beijing Jiaotong University, 100044, Beijing, China aut Neural Computing and Applications Springer London 26/1(2015-01-01), 141-149 0941-0643 26:1<141 2015 26 521 https://doi.org/10.1007/s00521-014-1705-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1705-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Gao Shigen State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, 100044, Beijing, China aut NATIONALLICENCE 700 1- Dong Hairong State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, 100044, Beijing, China aut NATIONALLICENCE 700 1- Ning Bin State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, 100044, Beijing, China aut NATIONALLICENCE 700 1- Chen Yao School of Electronic and Information Engineering, Beijing Jiaotong University, 100044, Beijing, China aut NATIONALLICENCE 700 1- Sun Xubin School of Electronic and Information Engineering, Beijing Jiaotong University, 100044, Beijing, China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/1(2015-01-01), 141-149 0941-0643 26:1<141 2015 26 521