Author:
Yang Junxia,Zhang Youpeng,Jin Yuxiang
Abstract
Aiming at the problem of the large tracking error of the desired curve for the automatic train operation (ATO) control strategy, an ATO control algorithm based on RBF neural network adaptive terminal sliding mode fault-tolerant control (ATSM-FTC-RBFNN) is proposed to realize the accurate tracking control of train operation curve. On the one hand, considering the state delay of trains in operation, a nonlinear dynamic model is established based on the mechanism of motion mechanics. Then, the terminal sliding mode control principle is used to design the ATO control algorithm, and the adaptive mechanism is introduced to enhance the adaptability of the system. On the other hand, RBFNN is used to adaptively approximate and compensate the additional resistance disturbance to the model so that ATO control with larger disturbance can be realized with smaller switching gain, and the tracking performance and anti-interference ability of the system can be enhanced. Finally, considering the actuator failure and the control input limitation, the fault-tolerant mechanism is introduced to further enhance the fault-tolerant performance of the system. The simulation results show that the control can compensate and process the nonlinear effects of control input saturation, delay, and actuator faults synchronously under the condition of uncertain parameters, external disturbances of the system model and can achieve a small error tracking the desired curve.
Funder
National Natural Science Foundation of China
Subject
Artificial Intelligence,Applied Mathematics,Industrial and Manufacturing Engineering,Human-Computer Interaction,Information Systems,Control and Systems Engineering
Reference38 articles.
1. Rail train operation control system based on communication;Gao;Mod. Urban Transit.,2007
2. Study on ATO braking model identification based on model selection and optimization techniques;Gao;J. China Railw. Soc.,2011
3. Research and development of automatic train operation for railway transportation systems: A survey
4. Extended fuzzy logic controller for high speed train
5. Research on automatic train operation based on model-free adaptive control;Shi;J. China Railw. Soc.,2016
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