JACIII Vol.21 No.2 pp. 258-265
doi: 10.20965/jaciii.2017.p0258


Pseudospectral Real-Time Optimal Energy Control with Safety Constraints for Heavy-Haul Trains

Rui Zhang, Jun Peng, Bin Chen, Hongtao Liao, and Zhiwu Huang

School of Information Science and Engineering, Central South University
Changsha, Hunan 410075, China

Corresponding author

July 5, 2016
November 8, 2016
Online released:
March 15, 2017
March 20, 2017
energy-efficient, safety, optimal control, radau pseudospectral method
Heavy-haul trains must be energy-efficient and safe during their operations. Owing to the multidimensional high-order nonlinear characteristic of heavy-haul trains, which include numerous cars, this paper proposes a uniform pseudospectral real-time closed-loop optimal control framework to minimize the energy consumption with control inputs and state constraints based on the Radau Pseudospectral Method (RPM). In the framework, in order to ensure safe running of the heavy-haul train, the desired in-train force and speed limit requirements are formulated as constraints of optimal control. Simultaneously, a constrained closed-loop optimal control is constructed by using the receding horizon control principle and pseudospectral observer, in which RPM is leveraged to obtain real-time optimal solutions. The effectiveness of the proposed approach is verified from simulation results.
Cite this article as:
R. Zhang, J. Peng, B. Chen, H. Liao, and Z. Huang, “Pseudospectral Real-Time Optimal Energy Control with Safety Constraints for Heavy-Haul Trains,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.2, pp. 258-265, 2017.
Data files:
  1. [1] M. Chou and X. Xia, “Optimal cruise control of heavy-haul trains equipped with electronically controlled pneumatic brake systems,” Control Engineering Practice, Vol.15, No.5, pp. 511-519, 2007.
  2. [2] X. Zhuan and X. Xia, “Cruise control scheduling of heavy haul trains,” IEEE Trans. on Control Systems Technology, Vol.14, No.4, pp. 757-766, 2006.
  3. [3] L. Zhang and X. Zhuan, “Optimal operation of heavy-haul trains equipped with electronically controlled pneumatic brake systems using model predictive control methodology,” IEEE Trans. on Control Systems Technology, Vol.22, No.1, pp. 13-22, 2014.
  4. [4] R. Liu and I. M. Golovitcher, “Energy-efficient operation of rail vehicles,” Transportation Research Part A: Policy and Practice, Vol.37, No.10, pp. 917-932, 2003.
  5. [5] P. Howlett, “Optimal strategies for the control of a train,” Automatica, Vol.32, No.4, pp. 519-532, 1996.
  6. [6] P. G. Howlett, P. J. Pudney, and V. Xuan, “Local energy minimization in optimal train control,” Automatica, Vol.45, No.11, pp. 2692-2698, 2009.
  7. [7] P. Gruber and M. M. Bayoumi, “Suboptimal control strategies for mul-tilocomotive powered trains,” IEEE Trans. on Automatic Control, Vol.27, No.3, pp. 536-546, 1982.
  8. [8] P. G. Howlett, I. P. Milroy, and P. J. Pudney, “Energy-efficient train control,” Control Engineering Practice, Vol.2, No.94, pp. 193-200, 1994.
  9. [9] M. Chou, X. Xia, and C. Kayser, “Modelling and model validation of heavy-haul trains equipped with electronically controlled pneumatic brake systems,” Control Engineering Practice, Vol.15, No.4, pp. 501-509, 2007.
  10. [10] G. T. Huntington and A. V. Rao, “Optimal reconfiguration of spacecraft formations using the gauss pseudospectral method,” J. of Guidance Control and Dynamics, Vol.31, No.3, pp. 689-698, 2008.
  11. [11] D. Garg, W. Hager, and A. Rao, “Gauss pseudospectral method for solving infinite-horizon optimal control problems,” Automatica, Vol.47, No.4, pp. 829-837, 2011.
  12. [12] T. Guo, J. Li, H. Baoyin, and F. Jiang, “Pseudospectral methods for trajectory optimization with interior point constraints: Verification and applications,” IEEE Trans. on Aerospace and Electronic Systems, Vol.3, No.49, pp. 2005-2017, 2013.
  13. [13] P. Han, J. Shan, and X. Meng, “Re-entry trajectory optimization using an hp-adaptive radau pseudospectral method,” Proc. of the Institution of Mechanical Engineers Part G, J. of Aerospace Engineering, Vol.227, No.10, pp. 1623-1636, 2012.
  14. [14] D. Garg, M. A. Patterson, C. Francolin, C. L. Darby, G. T. Huntington, W. W. Hager, and A. V. Rao, “Direct trajectory optimization and costate estimation of finite-horizon and infinite-horizon optimal control problems using a radau pseudospectral method,” Computational Optimization and Applications, Vol.360, No.2, pp. 217-223, 2006.
  15. [15] D. Garg, “Advances in global pseudospectral methods for optimal control,” 2011.
  16. [16] S. Li, Y. Zhu, and Y. Wang, “Rapid design and optimization of low-thrust rendezvous/interception trajectory for asteroid deflection missions,” Advances in Space Research, Vol.53, No.4, pp. 696-707, 2014.
  17. [17] G. Qi, W. Kang, N. S. Bedrossian, F. Fahroo, P. Sekhavat, and K. Bollino, “Pseudospectral optimal control for military and industrial applications,” Proc. of the IEEE Conf. on Decision and Control, pp. 4128-4142, 2008.
  18. [18] I. M. Ross, G. Qi, F. Fahroo, and W. Kang, “Practical stabilization through real-time optimal control,” American Control Conf., pp. 304-309, 2006.
  19. [19] Q. Gong, I. M. Ross, and W. Kang, “A unified pseudospectral framework for nonlinear controller and observer design,” American Control Conf., pp. 1943-1949, 2007.
  20. [20] K. Gao, Z. Huang, J. Wang, J. Peng, and W. Liu, “Decentralizedcontrol of heavy-haul trains with input constraints and communication delays,” Control Engineering Practice, Vol.21, No.4, pp. 420-427, 2013.
  21. [21] G. Qi, M. Ross, and W. Kang, “A pseudospectral observer for nonlinear systems,” Discrete and Continuous Dynamical Systems - Series B, Vol.3, No.3, 2007.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024