© The Institution of Engineering and Technology
High-speed electric multiple unit (HSEMU) is a complex non-linear system which runs under three typical operating modes (OMs) including traction, braking, and coasting. With the increasing traffic density of the high-speed railway, the conventionally manual control strategies may be inapplicable for the HSEMU to maintain a good running performance. To enhance the running performances, in this study, a novel multiple OM (MOM) running model is designed to accurately describe the non-linear relationship between running speed and controlling force. By utilising the advantages of adaptive neuro-fuzzy inference system (ANFIS) on non-linear modelling, this study proposes an MOM-ANFIS model of HSEMU. On the basis of the established MOM-ANFIS model, a new speed controller incorporated with OM selection mechanism is designed to achieve speed control of HSEMU followed by a stability analysis of the closed-loop system. Comparative experimental results using practical running data show that the proposed MOM-ANFIS model displays better modelling accuracy and the corresponding control strategy achieves improved speed control performances for the HSEMU.
References
-
-
1)
-
2. Yang, H., Zhang, K.P., Liu, H.E.: ‘Online regulation of high speed train trajectory control based on T–S fuzzy bilinear model’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (6), pp. 1496–1508.
-
2)
-
24. Chen, M.Y., Hybrid, A.: ‘ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering’, Inf. Sci., 2013, 220, (20), pp. 180–195.
-
3)
-
5. Song, Q., Song, Y.D., Cai, W.C.: ‘Adaptive backstepping control of train systems with traction/braking dynamics and uncertain resistive forces’, Veh. Syst. Dyn., Int. J. Veh. Mech. Mob., 2011, 49, (9), pp. 1441–1454.
-
4)
-
25. Khodayari, A., Ghaffari, A., Kazem, R., et al: ‘Improved adaptive neuro fuzzy inference system car-following behaviour model based on the driver-vehicle delay’, IET Intell. Transp. Syst., 2014, 8, (4), pp. 323–332.
-
5)
-
23. Chou, M., Xia, X.: ‘Optimal cruise control of heavy-haul trains equipped with electronically controlled pneumatic brake system’, Control Eng. Pract., 2007, 15, (5), pp. 511–519.
-
6)
-
18. Li, Y.F., Yang, B., Zheng, H., et al: ‘Extended-state-observer-based double-loop integral sliding-mode control of electronic throttle valve’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (5), pp. 2501–2510.
-
7)
-
21. Jang, J.S.R.: ‘ANFIS: adaptive-network-based fuzzy inference system’, IEEE Trans. Syst. Man Cybern., 1993, 23, (3), pp. 665–685.
-
8)
-
28. Clarke, D.W., Scattolini, R.: ‘Constrained receding-horizon predictive control’, IEE Proc. D Control Theory Appl., 1991, 138, (4), pp. 347–354.
-
9)
-
27. Fu, Y.T., Yang, H., Wang, D.H.: ‘Real-time optimal control of tracking running for high-speed electric multiple unit’, Inf. Sci., 2017, 376, (10), pp. 202–215.
-
10)
-
29. Narendra, K.S., Balakrishman, J.: ‘Adaptive control using multiple models’, IEEE Trans. Autom. Control, 1997, 42, (2), pp. 171–187.
-
11)
-
16. Wang, Y.J., Song, Y.D., Gao, H., et al: ‘Distributed fault-tolerant control of virtually and physically interconnected systems with application to high-speed trains under traction/braking failure’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (2), pp. 535–545.
-
12)
-
26. Jang, J.S.R., Sun, C.T.: ‘Neuro-fuzzy modeling and control’, Proc. IEEE, 1995, 83, (3), pp. 378–406.
-
13)
-
19. Tseng, C.S., Chen, B.S., Uang, H.J.: ‘Fuzzy tracking control design for nonlinear dynamic systems via T–S fuzzy model’, IEEE Trans. Fuzzy Syst., 2001, 35, (4), pp. 77–83.
-
14)
-
3. Sun, H.Q., Hou, Z.S., Li, D.Y.: ‘Coordinated iterative learning control schemes for train trajectory tracking with overspeed protection’, IEEE Trans. Autom. Sci. Eng., 2013, 10, (2), pp. 323–333.
-
15)
-
13. Ke, B.R., Lin, C.L., Lai, C.W.: ‘Optimization of train-speed trajectory and control for mass rapid transit systems’, Control Eng. Pract., 2011, 19, (7), pp. 675–687.
-
16)
-
12. Wen, S.H., Yang, J.W., Rad, A.B., et al: ‘Multi-model direct generalised predictive control for automatic train operation system’, IET Intell. Transp. Syst., 2015, 9, (1), pp. 86–94.
-
17)
-
6. Li, Y.F., Zhang, L., Zheng, H., et al: ‘Non-lane-discipline-based car-following model for electric vehicles in transportation-cyber-physical-systems’, IEEE Trans. Intell. Transp. Syst., 2017, .
-
18)
-
4. Albrecht, T., Binder, A., Gassel, C.: ‘Applications of real-time speed control in rail-bound public transportation systems’, IET Intell. Transp. Syst., 2013, 7, (3), pp. 305–314.
-
19)
-
20. Chen, S., Billings, S.: ‘Neural networks for nonlinear dynamic system modeling and identification’, Int. J. Control, 1992, 56, (2), pp. 319–346.
-
20)
-
10. Xu, D.Z., Jiang, B., Liu, F.: ‘Improved data driven model free adaptive constrained control for a solid oxide fuel cell’, IET Control Theory Appl., 2016, 10, (12), pp. 1412–1419.
-
21)
-
22. Yang, H., Fu, Y.T., Zhang, K.P., et al: ‘Speed tracking control using an ANFIS model for high-speed electric multiple unit’, Control Eng. Pract., 2014, 23, pp. 57–65.
-
22)
-
15. Ahn, C., Li, C.T., Peng, H.: ‘Optimal decentralized charging control algorithm for electrified vehicles connected to smart grid’, J. Power Sources, 2011, 196, (23), pp. 10369–10379.
-
23)
-
11. Yang, H., Zhang, F., Zhang, K.P., et al: ‘Predictive control using a distributed model for electric multiple unit’, Acta Autom. Sin., 2014, 40, (9), pp. 1912–1921.
-
24)
-
30. Dong, H.R., Ning, B., Cai, B.G., et al: ‘Automatic train control system development and simulation for high-speed railways’, IEEE Circuits Syst., 2010, 10, (2), pp. 6–18.
-
25)
-
8. Pan, D., Zheng, Y.P.: ‘Study on the mechanism of high-speed train following operation control’, J. China Railw. Soc., 2013, 35, (3), pp. 53–61.
-
26)
-
7. Li, Y.F., Zhang, L., Peeta, S., et al: ‘A car-following model considering the effect of electronic throttle opening angle under connected environment’, Nonlinear Dyn., 2016, 85, (4), pp. 2115–2125.
-
27)
-
14. Song, Q., Song, Y.D.: ‘Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures’, IEEE Trans. Neural Netw., 2011, 22, (12), pp. 2250–2261.
-
28)
-
17. Wu, M., Wang, C., Wang, S., et al: ‘Design and application of generalized predictive control strategy with closed-loop identification for burn-through point in sintering process’, Control Eng. Pract., 2012, 20, pp. 1065–1074.
-
29)
-
9. Wu, X., Shen, J., Li, Y.G., et al: ‘Data-driven modeling and predictive control for boiler–turbine unit’, IEEE Trans. Energy Convers., 2016, 28, (3), pp. 470–481.
-
30)
-
1. Ning, B., Tang, T., Dong, H.R., et al: ‘An introduction to parallel control and management for high-speed railway systems’, IEEE Trans. Intell. Transp. Syst., 2012, 12, (4), pp. 1473–1483.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2017.0121
Related content
content/journals/10.1049/iet-its.2017.0121
pub_keyword,iet_inspecKeyword,pub_concept
6
6