Open AccessCooperative Neuro-Adaptive Fault-Tolerant Tracking Control for a Train Platoon Under Actuator Saturation
Author(s) -
Tianxiang Wu,
Yang Liu,
Shaohua Chen,
He Wang,
Haodong Chen
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3231372
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
This study investigates the cooperative fault-tolerant tracking control problem of a train platoon in the presence of actuator failure, saturation limits, and unknown operating resistance. To construct a cooperative tracking control scheme, the proposed distributed controller is designed according to adjacent trains’ state information, including position, velocity, and acceleration. Radial basis function neural networks are introduced to tackle uncertain operational resistance, and the weight values of the neural network are updated online. Actuator failures with partial loss of effectiveness are estimated and compensated using adaptive methods. Another neuro-adaptive fault-tolerant control law is proposed to further solve the actuator saturation problem. The developed tracking controller remains fault-tolerant even under saturation limits while ensuring the stability of the closed-loop system. It is demonstrated using Lyapunov stability analysis that uniform ultimate boundedness is guaranteed for all signals of the closed-loop system. Numerical simulation results illustrate the effectiveness of the proposed control scheme.