The authors propose a novel multi-model direct generalised predictive control based on predictive function control (PFC) algorithm for automatic train operation system. The proposed method facilitates autonomous driving of a train through a given guidance trajectory. Firstly, they present a multi-model architecture based on fuzzy c-means clustering algorithm. In order to obtain the optimal number of sub-linear models, they apply Xie–Beni cluster validity index. In this regards, the multi-model set is established off-line. Secondly, the proper sub-linear model is selected as the predictive model by using switching performance index at each time slot. The control variables are calculated by direct generalised predictive controller based on PFC. The control algorithm is simple, and can reduce the on-line computation time by directly identifies the unknown parameters in the controller. It can avoid recursively solving the Diophantine equations. The calculation of compensation value becomes simple by introducing PFC. Finally, simulation results are provided to show the effectiveness of the proposed scheme.

References

1. 1)
  - D.W. Clarke , C. Mohtadi , P.S. Tuffs . Generalized predictive control – Parts I & II. Automatica , 137 - 160
2. 2)
  - 17. Xue, Z.K., Li, S.Y.: ‘A multimodel modeling approach to MIMO nonlinear systems’, Acta Electron. Sin., 2005, 33, (1), pp. 52–57.
3. 3)
  - 5. Yasunobu, S., Miyamoto, S., Ihara, H.: ‘A fuzzy control for train automatic stop control’, Trans. Soc. Instrum. Control Eng., 2002, E-2, (1), pp. 1–9.
4. 4)
  - 8. Wang, Y., Hou, Z.S.: ‘Terminal iterative learning control for station stop control of a train’. Proc. Symp. Learning Control at IEEE CDC, 2009, vol. 6, pp. 3119–3124.
5. 5)
  - 18. Ieroham, S.B., Rafael, B.L.: ‘A fuzzy-neural multi-model for nonlinear systems identification and control[J]’, Fuzzy Sets Syst., 2008, 159, (20), pp. 2650–2667 (doi: 10.1016/j.fss.2008.01.027).
6. 6)
  - 22. Novak, P.C.J., Bobal, V.: ‘Multiple model modeling and predictive control of the pH neutralization process’, Int. J. Math. Models Methods Appl. Sci., 2011, 5, pp. 1170–1179.
7. 7)
  - 10. Hongfeng, Q., Wei, X.: ‘Design of maglev automatic train operation system and research on predictive control algorithm’. 2011 IEEE Int. Conf. on Computer Science and Automation Engineering (CSAE), 2011, vol. 3, pp. 463–470.
8. 8)
  - 7. Wang, Y., Hou, Z.-S., Li, X.-Y.: ‘A novel automatic train operation algorithm based on iterative learning control theory’. Proc. IEEE Conf. Service Operations, Logistics and Informatics, 2008, pp. 1766–1770.
9. 9)
  - 25. Hong-Rui, W., Zhi-Wang, C., Jian-Xiong, L.: ‘Direct generalized predictive control of parameter adaptation for nonlinear systems’, Acta Autom. Sin., 2007, 33, (10), pp. 1110–1114.
10. 10)
  - B. Zhang , W.D. Zhang . Adaptive predictive functional control of a class of nonlinear systems. ISA Trans. , 2 , 175 - 83
11. 11)
  - 21. Li, N., Li, S.-Y., Li, Y.-G.: ‘Multi-model predictive control based on the Takagi-Sugeno fuzzy models: a case study’, Inf. Sci., 2004, 165, (3–4), pp. 247–263 (doi: 10.1016/j.ins.2003.10.011).
12. 12)
  - 13. Huo, H.-B., Zhu, X.-J., Hu, W.-Q., et al: ‘Nonlinear model predictive control of SOFC based on a Hammerstein model’, J. Power Sources, 2008, 185, (1), pp. 338–244 (doi: 10.1016/j.jpowsour.2008.06.064).
13. 13)
  - 29. Tehrani, R.D., Ferdowsi, M.H.: ‘Adaptive predictive function control with similar PI structure using unstructured system identification based on Laguerre functions’, Int. J. Control Theory Comput. Model., 2012, 2, (5), pp. 1–13 (doi: 10.5121/ijctcm.2012.2501).
14. 14)
  - 1. Banerjee, M.-K., Hoda, N.-E.: ‘Review of the automatic train control system for Cairo metro line 2’, Power Eng., 1998, 12, (5), pp. 217–228 (doi: 10.1049/pe:19980506).
15. 15)
  - 28. Mosca, E.: ‘Optimal, Predictive, and Adaptive Control’ (Prentice-Hall, Englewood Cliffs, 1995), 7, pp. 205–220.
16. 16)
  - 12. Huang, R., Patwardhan, S.C., Biegler, L.T.: ‘Robust stability of nonlinear model predictive control based on extended Kalman Filter’, J. Process Control, 2012, 22, (1), pp. 82–89 (doi: 10.1016/j.jprocont.2011.10.006).
17. 17)
  - R.D. Pascoe , T.N. Eichorn . What is communication-based train control?. IEEE Veh. Technol. Mag. , 4 , 16 - 21
18. 18)
  - 3. Dong, H.R., Ning, B., Cai, B.G., et al: ‘Automatic train control system development and simulation for high-speed railways’, IEEE Circuits Syst. Mag., 2010, 10, (2), pp. 6–18 (doi: 10.1109/MCAS.2010.936782).
19. 19)
  - 9. Wang, J., Qing, J.W., Li, H.B.: ‘Application of PID-type generalized predictive control algorithm in lateral semi-active suspension system for high-speed train’, J. Southwest Univ. Sci. Technol., 2010, 4, (2), pp. 0–16.
20. 20)
  - 24. Hui, Y., Kun-peng, Z., Xin, W., et al: ‘Generalized multiple-model predictive control method of high-speed train’, J. Chin. Railway Soc., 2011, 33, (8), pp. 80–87.
21. 21)
  - 23. Novak, P.C.J., Bobal, V.: ‘Modeling and predictive control of a nonlinear system using local model network’. Prerine 18th IFAC World Congress, 2011.
22. 22)
  - 14. Pan, Y., Wang, J.: ‘Model predictive control of unknown nonlinear dynamical systems based on recurrent neural networks’, IEEE Trans. Ind. Electron., 2012, 59, (8), pp. 3089–3101 (doi: 10.1109/TIE.2011.2169636).
23. 23)
  - 4. Wen, S.H., Yang, J.W., Rad, A.B., Chen, S.Y., Hao, P.C.: ‘Weighted multimodel predictive function control for automatic train operation system’, J. Appl. Math., 2014, 2014, pp. 8, Article ID 520627. doi:10.1155/2014/520627.
24. 24)
  - 16. Akpan, V.A., Hassapis, G.D.: ‘Nonlinear model identification and adaptive model predictive control using neural networks’, ISA Trans., 2011, 50, (2), pp. 177–194 (doi: 10.1016/j.isatra.2010.12.007).
25. 25)
  - 6. Jin, Y., Qian, Q.Q., He, Z.Y.: ‘Research on application of two-degree fuzzy neural network in ATO of high speed train’, J. Chin. Railway Soc., 2008, 30, (5), pp. 52–56.
26. 26)
  - 19. Zhen, Y.H., Wang, X., Li, S.Y., Jiang, J.G.: ‘Multiple models direct adaptive decoupling controller for a stochastic system’, Acta Autom. Sin., 2010, 36, (9), pp. 1295–1304 (doi: 10.3724/SP.J.1004.2010.01295).
27. 27)
  - 26. Velmurugan, T., Santhanam, T.: ‘Performance evaluation of K-means and fuzzy C-means clustering algorithms for statistical distributions of input data points’, Eur. J. Sci. Res., 2010, 46, (3), pp. 320–330.
28. 28)
  - 20. Chen, Q., Gao, L., Dougal, R.A., et al: ‘Multiple model predictive control for a hybrid proton exchange membrane fuel cell system’, J. Power Sources, 2009, 191, (2,15), pp. 473–482 (doi: 10.1016/j.jpowsour.2009.02.034).
29. 29)
  - 27. Chou, C.-H., Su, M.-C., Lai, E.: ‘A new cluster validity measure and its application to image compression’, Pattern Anal. Appl., 2004.
30. 30)
  - 15. Quevedo, D.E., Nesic, D.: ‘Input-to-state stability of packetized predictive control over unreliable networks affected by packet-dropouts’, IEEE Trans. Autom. Control, 2011, 56, (2), pp. 370–375 (doi: 10.1109/TAC.2010.2095950).

Multi-model direct generalised predictive control for automatic train operation system

References

Related content