Contraction-Based Adaptive Control for Automatic Train Operation

Wang, Longsheng; Xu, Haibo; Luo, Hengyu

doi:10.2316/journal.201.2016.1.201-2703

Cited by 3 publications

(1 citation statement)

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“…In literature [2] , adaptive terminal sliding mode control is used to realize accurate stopping considering passenger comfort under parametric uncertainty. Literature [3] uses iterative learning to overcome repeated uncertainty in the parking phase and achieve accurate parking. In reference [4] , a robust H  controller is designed to achieve good speed-tracking performance and driving smoothness with uncertainty considered.…”

Section: Introductionmentioning

confidence: 99%

Research on EMPC for multi-objective optimal control of high-speed trains under parametric uncertainty

Cheng,

Sun,

Wang

et al. 2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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The train operations are subject to several uncertain factors, which pose challenges to achieving the train’s multi-objective optimal control. To improve the service quality and transportation efficiency of high-speed railways, this paper proposes a control method to realize the multi-objective operation of high-speed trains in terms of safety, punctuality, energy efficiency, and driving smoothness considering the uncertainty. Firstly, by studying the kinematic model under uncertainty and operational constraints of the train, and combining them with multi-objective optimization theory, an optimal control model for the multi-objective operation of high-speed trains is established. Secondly, to solve the optimal control problem under the uncertainty, the nonlinear and non-convex optimal control problem for multi-objective operations of the train is reformulated as a robust economic model predictive control framework, which is then convexified into a computationally tractable convex model, and then the corresponding algorithm is designed to give the solutions. Finally, the proposed method is validated through simulation on a real high-speed rail line, and compared with tracking-type robust model predictive control. The simulation results demonstrate that the proposed method is capable of achieving safe, punctual, energy-efficient, and smooth operation of high-speed trains in the presence of uncertain factors.

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Section: Introductionmentioning

confidence: 99%