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Pseudospectral optimal train control

Author

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  • Goverde, Rob M.P.
  • Scheepmaker, Gerben M.
  • Wang, Pengling

Abstract

In the last decade, pseudospectral methods have become popular for solving optimal control problems. Pseudospectral methods do not need prior knowledge about the optimal control structure and are thus very flexible for problems with complex path constraints, which are common in optimal train control, or train trajectory optimization. Practical optimal train control problems are nonsmooth with discontinuities in the dynamic equations and path constraints corresponding to gradients and speed limits varying along the track. Moreover, optimal train control problems typically include singular solutions with a vanishing Hessian of the associated Hamiltonian. These characteristics make these problems hard to solve and also lead to convergence issues in pseudospectral methods. We propose a computational framework that connects pseudospectral methods with Pontryagin’s Maximum Principle allowing flexible computations, verification and validation of the numerical approximations, and improvements of the continuous solution accuracy. We apply the framework to two basic problems in optimal train control: minimum-time train control and energy-efficient train control, and consider cases with short-distance regional trains and long-distance intercity trains for various scenarios including varying gradients, speed limits, and scheduled running time supplements. The framework confirms the flexibility of the pseudospectral method with regards to state, control and mixed algebraic inequality path constraints, and is able to identify conditions that lead to inconsistencies between the necessary optimality conditions and the numerical approximations of the states, costates, and controls. A new approach is proposed to correct the discrete approximations by incorporating implicit equations from the optimality conditions. In particular, the issue of oscillations in the singular solution for energy-efficient driving as computed by the pseudospectral method has been solved.

Suggested Citation

  • Goverde, Rob M.P. & Scheepmaker, Gerben M. & Wang, Pengling, 2021. "Pseudospectral optimal train control," European Journal of Operational Research, Elsevier, vol. 292(1), pages 353-375.
  • Handle: RePEc:eee:ejores:v:292:y:2021:i:1:p:353-375
    DOI: 10.1016/j.ejor.2020.10.018
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    Citations

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    Cited by:

    1. Dimitris Kouzoupis & Ishan Pendharkar & Francesco Corman, 2023. "TTOBench—an Open-Source Library for Train Trajectory Optimization," SN Operations Research Forum, Springer, vol. 4(4), pages 1-16, December.
    2. Gabriel Popa & Constantin Udriste & Ionel Tevy, 2021. "Train Bi-Control Problem on Riemannian Setting," Mathematics, MDPI, vol. 9(22), pages 1-12, November.
    3. Constantin Udriste & Ionel Tevy & Paun Antonescu, 2023. "Optimal Control Problem for Minimization of Net Energy Consumption at Metro," Mathematics, MDPI, vol. 11(4), pages 1-11, February.
    4. Lian, Deheng & Mo, Pengli & D’Ariano, Andrea & Gao, Ziyou & Yang, Lixing, 2024. "Energy-saving time allocation strategy with uncertain dwell times in urban rail transit: Two-stage stochastic model and nested dynamic programming framework," European Journal of Operational Research, Elsevier, vol. 317(1), pages 219-242.

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