Model Free Adaptive Iterative Learning Control Based Fault-Tolerant Control for Subway Train With Speed Sensor Fault and Over-Speed Protection

• Published in: IEEE transactions on automation science and engineering Vol. 21; no. 1; pp. 168 - 180
• Main Authors: Zheng, Jianmin, Hou, Zhongsheng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Aerodynamics; Algorithms; Braking; Compensation; Control methods; Controllers; Effectiveness; Fault tolerance; fault-tolerant control (FTC); Iterative learning control; Iterative methods; Learning; Mathematical analysis; Model free adaptive iterative learning control (MFAILC); over-speed protection; Public transportation; Radial basis function networks; radial basis function neural networks (RBFNNs); Railway stations; Robustness (mathematics); Sensors; speed sensor fault; Subway stations; subway train control; Tracking errors; Traction; Trajectory control; Uncertainty
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2022.3225288

A model free adaptive iterative learning control based fault-tolerant control (MFAILC-FTC) scheme for subway train speed tracking with speed sensor fault and over-speed protection is proposed. Firstly, the train dynamics is transformed into a compact form dynamic linearization (CFDL) data model by applying the concept of pseudo-partial derivative (PPD). If speed sensor fault occurs, the fault function is approximated by the trained RBFNNs under normal condition and the output data of the train system with fault, which serves as a compensation for the proposed MFAILC-FTC scheme. Then, over-speed protection mechanism is developed to ensure that the train operates within safe speed range. Furthermore, the constraint on traction/braking force is also taken into account. Through rigorous mathematical analysis, it is proved that the proposed MFAILC-FTC method with over-speed protection mechanism can ensure the train speed tracking error converges along the iteration axis, which implies the train operates safely and reliably. Finally, the simulation results further demonstrate the effectiveness of the proposed algorithm. Note to Practitioners-Subway train as a practical engineering system with short distance between two stations, starts and stops frequently, has the outstanding repetitive operation pattern, and it is unavoidable subject to speed sensor fault, aerodynamic issues, constraint on output speed and traction/braking force. Nevertheless, few works have considered these factors simultaneously, and a lot of data contain valuable operation information are generated during the train operation, this motives the work of this note. On account of the repetitive operation features of subway trains, the control schemes of speed trajectory tracking are handled under MFAILC framework, which is a pure data-driven model free control methodology. By constructing the RBFNNs-based fault function estimation mechanism, a robust compensation term is designed in the fault-tolerant controller. Taking the safe operation of subway trains into account, an over-speed protection term with trigger mechanism is added to the fault-tolerant controller. To further enhance the application, the constraint on traction/braking force is addressed as well. Without requirement of the train dynamics model, the theoretical analyses and simulation results have confirmed the effectiveness and the feasibility of the proposed data-driven control approach. In the future work, we will focus on verifying the proposed control strategy and addressing some other practical problems, for instance, the energy-efficiency and exogenous disturbances during the train operation.