A neural network-based adaptive fault-tolerant cooperation control for multiple trains with unknown parameters

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Abstract

For the problems of parametric uncertainties and actuator faults encountered during high-speed train operation, this paper proposes a neural network-based adaptive fault-tolerant control strategy for cooperation of multiple trains. First, a multi-train model with state constraints, disturbances, and actuator faults was established, and the radial basis function neural network was utilized to fit the unknown disturbance. Subsequently, the auxiliary control signal was introduced to compensate the impact of actuator faults on train. Then, when designing adaptive laws, the variations of basic resistance parameters were fully considered. By integrating adaptive laws with the adaptive fault-tolerant controller, the effect of disturbances on multi-train systems can be dynamically eliminated. The Lyapunov method was established to prove the stability of multi-train systems. Simulation results show that the proposed adaptive fault-tolerant control strategy can maintain the stability of multi-train systems and achieve cooperation of trains with actuator faults.

Keywords

high-speed trains / neural network / unknown parameters / actuator faults / adaptive fault-tolerant control

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References

[1] H. Wang, Y. Li, J. Ren, Machine learning for fault diagnosis of high-speed train traction systems: a review, Front. Eng. Manage., 11 (2024), 62-78. https://doi.org/10.1007/s42524-023-0256-2

[2] S. Gao, H. Dong, Y. Chen, B. Ning, Approximation-based robust adaptive automatic train control: an approach for actuator saturation, IEEE Trans. Intell. Transp. Syst., 14 (2013), 1733-1742. https://doi.org/10.1109/TITS.2013.2266255

[3] S. Li, L. Yang, Z. Gao, L. Li, Optimal guaranteed cost cruise control for high-speed train movement, IEEE Trans. Intell. Transp. Syst., 17 (2016), 2879-2887. https://doi.org/10.1109/TITS.2016.2527827

[4] M. Faieghi, A. Jalali, S. Mashhadi, Robust adaptive cruise control of high speed trains, ISA Trans., 53 (2014), 533-541. https://doi.org/10.1016/j.isatra.2013.12.007

[5] Q. Song, Y. Song, T. Tang, B. Ning, Computationally inexpensive tracking control of high-speed trains with traction/braking saturation, IEEE Trans. Intell. Transp. Syst., 12 (2011), 1116-1125. https://doi.org/10.1016/10.1109/TITS.2011.2143409

[6] W. Bai, Z. Lin, H. Dong, B. Ning, Distributed cooperative cruise control of multiple high-speed trains under a state-dependent information transmission topology, IEEE Trans. Intell. Transp. Syst., 20 (2019), 2750-2763. https://doi.org/10.1109/TITS.2019.2893583

[7] S. Li, L. Yang, Z. Gao, Coordinated cruise control for high-speed train movements based on a multi-agent model, Transp. Res. Part C Emerging Technol., 56 (2015), 281-292. https://doi.org/10.1016/j.trc.2015.04.016

[8] G. Guo, D. Li, Adaptive sliding mode control of vehicular platoons with prescribed tracking performance, IEEE Trans. Veh. Technol., 68 (2019), 7511-7520. https://doi.org/10.1109/TVT.2019.2921816

[9] Y. Liu, Y. Zhou, S. Su, J. Xun, T. Tang, An analytical optimal control approach for virtually coupled high-speed trains with local and string stability, Transp. Res. Part C Emerging Technol., 125 (2021), 102886. https://doi.org/10.1016/j.trc.2020.102886

[10] H. Hu, Z. He, K. Wang, X. Ma, S. Gao, Power-quality impact assessment for high-speed railway associated with high-speed trains using train timetable-Part II: verifications, estimations and applications, IEEE Trans. Power Del., 31 (2016), 1482-1492. https://doi.org/10.1109/TPWRD.2015.2472961

[11] S. Li, X. Li, K. Li, Robust output feedback cruise control for high-speed train movement with uncertain parameters, Chin. Phys. B, 21 (2015), 010503. https://doi.org/10.1088/1674-1056/24/1/010503

[12] X. Lin, C. Ma, Q. Wang, Dual jitter suppression mechanism-based cooperation control for multiple high-speed trains with parametric uncertainty, Mathematics, 22 (2023), 1786. https://doi.org/10.3390/math11081786

[13] Y. Chen, D. Huang, C. Xu, H. Dong, Iterative learning tracking control of high-speed trains with nonlinearly parameterized uncertainties and multiple time-varying delays, IEEE Trans. Intell. Transp. Syst., 23 (2022), 20476-20488. https://doi.org/10.1109/TITS.2022.3183608

[14] H. Liu, L. Yang, H. Yang, Cooperative optimal control of the following operation of high-speed trains, IEEE Trans. Intell. Transp. Syst., 23 (2022), 17744-17755. https://doi.org/10.1109/TITS.2022.3163971

[15] Y. Cao, J. Wen, L. Ma, Tracking and collision avoidance of virtual coupling train control system, Future Gener. Comput. Syst., 120 (2021), 76-90. https://doi.org/10.1016/j.future.2021.02.014

[16] H. Ji, Z. Hou, R. Zhang, Adaptive iterative learning control for high-speed trains with unknown speed delays and input saturations, IEEE Trans. Autom. Sci. Eng., 13 (2016), 260-273. https://doi.org/10.1109/TASE.2014.2371816

[17] H. Zhao, X. Dai, Y. Zhao, Sliding-mode adaptive control for multiple high-speed trains with state constraints and input saturation, Int. J. Control, Autom. Syst., 21 (2023), 3607-3616. https://doi.org/10.1007/s12555-022-0217-2

[18] S. Gao, H. Dong, B. Ning, Q. Zhang, Cooperative prescribed performance tracking control for multiple high-speed trains in moving block signaling system, IEEE Trans. Intell. Transp. Syst., 20 (2019), 2740-2749. https://doi.org/10.1109/TITS.2018.2877171

[19] D. Huang, Y. Jia, X. Li, L. Zhu, Prescribed performance tracking control for adjacent virtual coupling high-speed trains with input saturation, Int. J. Robust Nonlinear Control, 33 (2023), 9450-9468. https://doi.org/10.1002/rnc.6862

[20] J. Yang, Y. Zhang, Y. Jin, Cooperative robust adaptive control of multiple trains based on RBFNN position output constraints, Expert Syst., 41 (2022), 13034. https://doi.org/10.1111/exsy.13034

[21] D. Li, G. Guo, Prescribed performance concurrent control of connected vehicles with nonlinear third-order dynamics, IEEE Trans. Veh. Technol., 69 (2020), 14793-14802. https://doi.org/10.1109/TVT.2020.3040302

[22] J. Zhang, H. Yang, K. Zhang, C. Xie, Tracking control for high-speed train with coupler constraints, IEEE Trans. Intell. Transp. Syst., 25 (2024), 14654-14668. https://doi.org/10.1109/TITS.2024.3392629

[23] X. Guo, J. Wang, F. Liao, CNN-based distributed adaptive control for vehicle-following platoon with input saturation, IEEE Trans. Intell. Transp. Syst., 19 (2018), 3121-3132. https://doi.org/10.1109/TITS.2017.2772306

[24] L. Zhu, D. Huang, X. Li, Q. Wang, Cooperative operation control of virtual coupling high-speed trains with input saturation and full-state constraints, IEEE Trans. Autom. Sci. Eng., 21 (2024), 3497-3510. https://doi.org/10.1109/TASE.2023.3280188

[25] H. Zhao, X. Dai, Event-triggered adaptive control for multiple high-speed trains with deception attacks in bottleneck sections, Inf. Sci., 547 (2021), 470-481. https://doi.org/10.1016/j.ins.2020.08.012

[26] W. Bai, H. Dong, J. Lu, Y. Li, Event-triggering communication based distributed coordinated control of multiple high-speed trains, IEEE Trans. Veh. Technol., 70 (2021), 8556-8566. https://doi.org/10.1109/TVT.2021.3099529

[27] W. Wang, J. Le, Z. Wang, Event-triggered consensus control for high-speed train with time-varying actuator fault, IEEE Access, 8 (2020), 50553-50564. https://doi.org/10.1109/ACCESS.2020.2979890

[28] D. Li, P. Li, W. Cai, H. Dong, B. Liu, P. Ma, Neural adaptive fault tolerant control for high speed trains considering actuation notches and antiskid constraints, IEEE Trans. Intell. Transp. Syst., 20 (2019), 1706-1718. https://doi.org/10.1109/TITS.2018.2832635

[29] Z. Mao, X. Yan, B. Jiang, M. Chen, Adaptive fault-tolerant sliding-mode control for high-speed trains with actuator faults and uncertainties, IEEE Trans. Intell. Transp. Syst., 21 (2019), 2449-2460. https://doi.org/10.1109/TITS.2019.2918543

[30] X. Yao, S. Li, X. Li, Composite adaptive anti-disturbance fault tolerant control of high-speed trains with multiple disturbances, IEEE Trans. Intell. Transp. Syst., 23 (2022), 21799-21809. https://doi.org/10.1109/TITS.2022.3174265

[31] L. Zhu, X. Li, D. Huang, H. Dong, L. Cai, Distributed cooperative fault-tolerant control of high-speed trains with input saturation and actuator faults, IEEE Trans. Intell. Veh., 8 (2022), 1241-1251. https://doi.org/10.1109/TIV.2022.3168550

[32] S. Su, D. Wang, Y. Cao, Y. Zhou, T. Tang, Adaptive fault-tolerant fixed-time cruise control for virtually coupled train set, Transp. Res. Part C Emerging Technol., 156 (2023), 104348. https://doi.org/10.1016/j.trc.2023.104348

[33] Z. Gao, Y. Zhang, G. Guo, Finite-time fault-tolerant prescribed performance control of connected vehicles with actuator saturation, IEEE Trans. Veh. Technol., 72 (2023), 1438-1448. https://doi.org/10.1109/TVT.2022.3209802