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Volume 6 Issue 4
Jul.  2019

IEEE/CAA Journal of Automatica Sinica

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Shigen Gao, Yuhan Hou, Hairong Dong, Sebastian Stichel and Bin Ning, "High-Speed Trains Automatic Operation with Protection Constraints: A Resilient Nonlinear Gain-based Feedback Control Approach," IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 992-999, Aug. 2019. doi: 10.1109/JAS.2019.1911582
Citation: Shigen Gao, Yuhan Hou, Hairong Dong, Sebastian Stichel and Bin Ning, "High-Speed Trains Automatic Operation with Protection Constraints: A Resilient Nonlinear Gain-based Feedback Control Approach," IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 992-999, Aug. 2019. doi: 10.1109/JAS.2019.1911582

High-Speed Trains Automatic Operation with Protection Constraints: A Resilient Nonlinear Gain-based Feedback Control Approach

doi: 10.1109/JAS.2019.1911582
Funds:  This work was supported jointly by the National Natural Science Foundation of China (61703033, 61790573), Beijing Natural Science Foundation (4192046), Fundamental Research Funds for Central Universities (2018JBZ002), and State Key Laboratory of Rail Traffic Control and Safety (RCS2018ZT013), Beijing Jiaotong University
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  • This paper addresses the control design for automatic train operation of high-speed trains with protection constraints. A new resilient nonlinear gain-based feedback control approach is proposed, which is capable of guaranteeing, under some proper non-restrictive initial conditions, the protection constraints control raised by the distance-to-go (moving authority) curve and automatic train protection in practice. A new hyperbolic tangent function-based model is presented to mimic the whole operation process of high-speed trains. The proposed feedback control methods are easily implementable and computationally inexpensive because the presence of only two feedback gains guarantee satisfactory tracking performance and closed-loop stability, no adaptations of unknown parameters, function approximation of unknown nonlinearities, and attenuation of external disturbances in the proposed control strategies. Finally, rigorous proofs and comparative simulation results are given to demonstrate the effectiveness of the proposed approaches.

     

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  • [1]
    R. R. Nair and L. Behera, " Robust adaptive gain higher order sliding mode observer based control-constrained nonlinear model predictive control for spacecraft formation flying,” IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 1, pp. 367–381, 2018. doi: 10.1109/JAS.2016.7510253
    [2]
    L. Chen and Q. Wang, " Adaptive robust control for a class of uncertain mimo non-affine nonlinear systems,” IEEE/CAA Journal of Automatica Sinica, vol. 3, no. 1, pp. 105–112, 2016. doi: 10.1109/JAS.2016.7373768
    [3]
    R. He, B. Ai, G. Wang, K. Guan, Z. Zhong, A. F. Molisch, C. BrisoRodriguez, and C. P. Oestges, " High-speed railway communications: From GMS-R to LTE-R,” IEEE VehIcular Technology Magazine, vol. 11, no. 3, pp. 49–58, 2016. doi: 10.1109/MVT.2016.2564446
    [4]
    P. T. Dat, A. Kanno, N. Yamamoto, and T. Kawanishi, " WDM RoF-MMW and linearly located distributed antenna system for future high-speed railway communications,” IEEE Communications Magazine, vol. 53, no. 10, pp. 86–94, 2015. doi: 10.1109/MCOM.2015.7295468
    [5]
    S. Zhao, X. Huang, Y. Fang, and J. Li, " A control scheme for a high speed railway traction system based on high power pmsm, ” in Power Electronics Systems and Applications (PESA), 2015 6th International Conference on. IEEE, 2015, pp. 1–8.
    [6]
    H. Ji, Z. Hou, and R. Zhang, " Adaptive iterative learning control for high-speed trains with unknown speed delays and input saturations,” IEEE Transactions on Automation Science and Engineering, vol. 13, no. 1, pp. 260–273, 2016. doi: 10.1109/TASE.2014.2371816
    [7]
    T. Liu and Z. Jiang, " Distributed control of nonlinear uncertain systems: A cyclic-small-gain approach,” IEEE/CAA Journal of Automatica Sinica, vol. 1, no. 1, pp. 46–53, 2014. doi: 10.1109/JAS.2014.7004619
    [8]
    Y. Xia, " Cloud control systems,” IEEE/CAA Journal of Automatica Sinica, vol. 2, no. 2, pp. 134–142, 2015. doi: 10.1109/JAS.2015.7081652
    [9]
    H. Dong, B. Ning, B. Cai, and Z. Hou, " Automatic train control system development and simulation for high-speed railways,” IEEE Circuits and Systems Magazine, vol. 10, no. 2, pp. 6–18, 2010. doi: 10.1109/MCAS.2010.936782
    [10]
    S. Yasunobu, S. Miyamoto, and H. Ihara, " Fuzzy control for automatic train operation system,” Control in Transportation Systems, vol. 62, no. 7, pp. 33–39, 1984.
    [11]
    H. Oshima, S. Yasunobu, and S. I. Sekino, " Automatic train operation system based on predictive fuzzy control, ” in Processdings of the International Workshop on Artificial Intelligence for Industrial Applications, IEEE, 1988, pp. 485–489.
    [12]
    S. Sekine, N. Imasaki, and T. Endo, " Application of fuzzy neural network control to automatic train operation and tuning of its control rules, ” in Processdings of the International Joint Conference of the 4th IEEE International Conference on Fuzzy Systems and the 2nd International Fuzzy Engineering Symposium, 1995, pp. 1741–1746.
    [13]
    C. D. Yang and Y. P. Sun, " Robust cruise control of high speed train with hardening/softening nonlinear coupler, ” in Processdings of the American Control Conference, 1999, pp. 2200–2204.
    [14]
    S. H. Han, Y. S. Byen, J. H. Baek, T. K. An, S. G. Lee, and H. J. Park, " An optimal automatic train operation (ato) control using genetic algorithms (ga), ” in Proceedings of the IEEE Region 10 Conference TENCON 99, vol. 1, 1999, pp. 360–362.
    [15]
    C. Yang and Y. Sun, " Mixed H2/H cruise controller design for high speed train,” International Journal of Control, vol. 74, no. 9, pp. 905–920, 2001. doi: 10.1080/00207170010038703
    [16]
    X. U. Yanping, X. Zhao, L. Wang, X. Liu, and Q. Zhang, " Optimal control of automatic train operation based on multi-scale dynamic programming, ” in Proceedings of the 33th Chinese Control Conference, 2014, pp. 3429–3433.
    [17]
    S. Lu, S. Hillmansen, T. K. Ho, and C. Roberts, " Single-train trajectory optimization,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 2, pp. 743–750, 2013. doi: 10.1109/TITS.2012.2234118
    [18]
    P. Wu, Q. Wang, and X. Feng, " Automatic train operation based on adaptive terminal sliding mode control,” International Journal of Automation and Computing, vol. 12, no. 2, pp. 142–148, 2015. doi: 10.1007/s11633-015-0877-y
    [19]
    X. H. Yan, B. G. Cai, B. Ning, and W. Shangguan, " Moving horizon optimization of dynamic trajectory planning for high-speed train operation,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 5, pp. 1258–1270, 2016. doi: 10.1109/TITS.2015.2499254
    [20]
    M. Ganesan, D. Ezhilarasi, and J. Benni, " Hybrid model reference adaptive second order sliding mode controller for automatic train operation,” IET Control Theory &Applications, vol. 11, no. 8, pp. 1222–1233, 2017.
    [21]
    J. Meng, R. Xu, D. Li, and X. Chen, " Combining the matter-element model with the associated function of performance indices for automatic train operation algorithm,” IEEE Transactions on Intelligent Transportation Systems, 2018.
    [22]
    M. Wang and A. Yang, " Dynamic learning from adaptive neural control of robot manipulators with prescribed performance,” IEEE Transactions on Systems,Man,and Cybernetics:Systems, vol. 47, no. 8, pp. 2244–2255, 2017. doi: 10.1109/TSMC.2016.2645942
    [23]
    S. He, M. Wang, S.-L. Dai, and F. Luo, " Leader-follower formation control of USVs with prescribed performance and collision avoidance,” IEEE Transactions on Industrial Informatics, 2018.
    [24]
    S. Gao, H. Dong, B. Ning, and Q. Zhang, " Cooperative prescribed performance tracking control for multiple high-speed trains in moving block signaling system,” IEEE Transactions on Intelligent Transportation Systems, 2018. doi: 10.1109/TITS.2018.2877171
    [25]
    C. P. Bechlioulis and G. A. Rovithakis, " Robust adaptive control of feedback linearizable mimo nonlinear systems with prescribed performance,” IEEE Transactions on Automatic Control, vol. 53, no. 9, pp. 2090–2099, 2008. doi: 10.1109/TAC.2008.929402
    [26]
    R. Bai, " Neural network control-based adaptive design for a class of dc motor systems with the full state constraints,” Neurocomputing, vol. 168, pp. 65–69, 2015. doi: 10.1016/j.neucom.2015.04.090
    [27]
    W. He, Y. Chen, and Z. Yin, " Adaptive neural network control of an uncertain robot with full-state constraints,” IEEE Transactions on Cybernetics, vol. 46, no. 3, pp. 620–629, 2016. doi: 10.1109/TCYB.2015.2411285
    [28]
    Z.-L. Tang, S. S. Ge, K. P. Tee, and W. He, " Robust adaptive neural tracking control for a class of perturbed uncertain nonlinear systems with state constraints,” IEEE Transactions on Systems,Man,and Cybernetics:Systems, vol. 46, no. 12, pp. 1618–1629, 2016. doi: 10.1109/TSMC.2015.2508962
    [29]
    D.-P. Li and D.-J. Li, " Adaptive neural tracking control for nonlinear time-delay systems with full state constraints,” IEEE Transactions on Systems,Man,and Cybernetics:Systems, vol. 47, no. 7, pp. 1590–1601, 2017. doi: 10.1109/TSMC.2016.2637063
    [30]
    C. P. Bechlioulis and G. A. Rovithakis, " A low-complexity global approximation-free control scheme with prescribed performance for unknown pure feedback systems,” Automatica, vol. 50, no. 4, pp. 1217–1226, 2014. doi: 10.1016/j.automatica.2014.02.020
    [31]
    S. Gao, H. Dong, Y. Chen, B. Ning, and G. Chen, " Adaptive and robust automatic train control systems with input saturation,” Control and Intelligent Systems, vol. 41, no. 2, pp. 103–111, 2013.
    [32]
    S. Gao, H. Dong, Y. Chen, B. Ning, G. Chen, and X. Yang, " Approximation-based robust adaptive automatic train control: an approach for actuator saturation,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 4, pp. 1733–1742, 2013. doi: 10.1109/TITS.2013.2266255
    [33]
    S. Gao, H. Dong, and B. Ning, " Neural adaptive dynamic surface control for uncertain strict-feedback nonlinear systems with nonlinear output and virtual feedback errors,” Nonlinear Dynamics, vol. 90, no. 4, pp. 2851–2867, 2017. doi: 10.1007/s11071-017-3847-9
    [34]
    E. D. Sontag, Mathematical control theory: deterministic finite dimensional systems. Springer Science & Business Media, 1998.
    [35]
    T. Hu, A. R. Teel, and L. Zaccarian, " Anti-windup synthesis for linear control systems with input saturation: achieving regional, nonlinear performance,” Automatica, vol. 44, no. 2, pp. 512–519, 2008. doi: 10.1016/j.automatica.2007.06.003
    [36]
    Y. Li and Z. Lin, " An asymmetric lyapunov function for linear systems with asymmetric actuator saturation,” International Journal of Robust and Nonlinear Control, vol. 28, no. 5, pp. 1624–1640, 2018. doi: 10.1002/rnc.v28.5
    [37]
    Y. Li and Z. Lin, " Design of saturation-based switching anti-windup gains for the enlargement of the domain of attraction,” IEEE Transactions on Automatic Control, vol. 58, no. 7, pp. 1810–1816, 2013. doi: 10.1109/TAC.2012.2231532
    [38]
    S. Gao, H. Dong, S. Lyu, and B. Ning, " Truncated adaptation design for decentralised neural dynamic surface control of interconnected nonlinear systems under input saturation,” International Journal of Control, vol. 89, no. 7, pp. 1447–1466, 2016. doi: 10.1080/00207179.2015.1135507

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    Highlights

    • A resilient nonlinear gain-based feedback control is designed for the automatic train operation of high-speed trains.
    • A new hyperbolic tangent function-based model is established to circumvent the countermarch motion when a train stops.
    • The proposed feedback control is easily implementable and computationally inexpensive with two feedback gains in spite of unknown nonlinearities.

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