Optimization of Train Regulation and Energy Usage of Metro Lines Using an Adaptive-Optimal-Control Algorithm

Lin, Wenwei; Sheu, Jih-Wen

doi:10.1109/tase.2011.2160537

Cited by 69 publications

(24 citation statements)

References 18 publications

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“…Scheepmaker [11] incorporated energy-efficient train operation into the railway timetable by distributing the time supplements into segments, and the robustness of the generated timetable was analyzed. Similar studies can be found in [12,13]. Su [14] proposed a cooperative train control model to efficiently use the regenerative energy by adjusting the departure time of the accelerating train.…”

Section: %mentioning

confidence: 54%

Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Simulation Model

Tang

Wang

2016

Energies

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Abstract:Increasing attention is being paid to the energy efficiency in metro systems to reduce the operational cost and to advocate the sustainability of railway systems. Classical research has studied the energy-efficient operational strategy and the energy-efficient system design separately to reduce the traction energy consumption. This paper aims to combine the operational strategies and the system design by analyzing how the infrastructure and vehicle parameters of metro systems influence the operational traction energy consumption. Firstly, a solution approach to the optimal train control model is introduced, which is used to design the Optimal Train Control Simulator(OTCS). Then, based on the OTCS, the performance of some important energy-efficient system design strategies is investigated to reduce the trains' traction energy consumption, including reduction of the train mass, improvement of the kinematic resistance, the design of the energy-saving gradient, increasing the maximum traction and braking forces, introducing regenerative braking and timetable optimization. As for these energy-efficient strategies, the performances are finally evaluated using the OTCS with the practical operational data of the Beijing Yizhuang metro line. The proposed approach gives an example to quantitatively analyze the energy reduction of different strategies in the system design procedure, which may help the decision makers to have an overview of the energy-efficient performances and then to make decisions by balancing the costs and the benefits.

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Section: %mentioning

confidence: 54%

Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Simulation Model

Tang

Wang

2016

Energies

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“…In addition to using the Pontryagin maximum principle, Howlett, Pudney, and Vu (2009) provided an analytical method for the problem with more than one steep slope, and a local optimisation principle was used to solve the energy-efficient driving strategy for each part of the route. Lin and Sheu (2011) proposed an adaptive optimal control algorithm to regulate the train operation with less energy consumption. The method can be used to optimise the train regulator by adjusting the running time and dwell time.…”

Section: Literature Reviewmentioning

confidence: 99%

An optimisation method for train scheduling with minimum energy consumption and travel time in metro rail systems

Yang

Ning

et al. 2015

Transportmetrica B: Transport Dynamics

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Both energy consumption and travel time are important indices to evaluate the efficiency of operations of metro rail systems. This paper proposes an optimisation method to schedule trains for reducing the energy consumption and travel time. Firstly, we formulate an integer programming model with timetable and speed control. Secondly, we design an optimal train control algorithm and an adaptive genetic algorithm to find a good solution. Finally, we conduct numerical examples based on the real-life operation data from the Beijing Yizhuang metro rail line of China. The results illustrate that the proposed approach can reduce energy consumption by 7.31% and reduce travel time by 3.26% in comparison with the current operation strategy.

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“…The control objective is to seek the control input of train motion dynamics (7) and (8) to be the optimal solution of the following objective: (9) subject to (10) (11) where , is the desired output of the th train, is the upper bound of the state, i.e., the maximum speed limit, of the th train, is the minimum safe headway of two adjacent trains, . In a word, the objective is to choose the control input to drive the position of the train to track its desired trajectory under constraints of overspeed protection for a train and safe headway between any two adjacent trains.…”

Section: B Control Objectivementioning

confidence: 99%

“…And then closed-loop control methods can be applied to drive trains to track this specified speed profile. A variety of speed regulation algorithms have been developed, such as PID control [5], fuzzy logic control [6], [7], linear quadratic regulators (LQRs) [8], [9], nonlinear output regulator [10], reinforcement learning [11], and control [12]. Most of these approaches are based on the Newtonian mechanics model of the train, and thus their control performances depend on the accuracy of the train models.…”

Section: Introductionmentioning

confidence: 99%

Coordinated Iterative Learning Control Schemes for Train Trajectory Tracking With Overspeed Protection

Sun

Hou

2013

IEEE Trans. Automat. Sci. Eng.

113

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This work embodies the overspeed protection and safe headway control into an iterative learning control (ILC) based train trajectory tracking algorithm to satisfy the high safety requirement of high-speed railways. First, a D-type ILC scheme with overspeed protection is proposed. Then, a corresponding coordinated ILC scheme with multiple trains is studied to keep the safe headway. Finally, the control scheme under traction/braking force constraint is also considered for this proposed ILC-based train trajectory tracking strategy. Rigorous theoretical analysis has shown that the proposed control schemes can guarantee the asymptotic convergence of train speed and position to its desired profiles without requirement of the physical model aside from some mild assumptions on the system. Effectiveness is further evaluated through simulations.Note to Practitioners-This work was motivated by the outstanding repetitive operation pattern and the high safety requirements of high-speed railways. By utilizing the repetitive operation feature of a train, iterative learning control strategy is applied to improve control performance by learning repetitive information. To avoid excessive speed, which is regarded as a severe unsafe factor for the high-speed train, an overspeed penalty is added as a state constraint. To keep safe headway between adjacent trains, the adjacent trains' information is used to construct the coordinated iterative learning control strategy. To further enhance the applicability, the corresponding control scheme under traction/braking force constraint is also presented. In addition, this approach is a data driven model-free control method. The above method can also be applied to other repetitive operation systems with state constraint, input constraint, or requirement of coordination operation. Although the feasibility and effectiveness of this ILC-based train control method has been proved through the rigorous theoretical analyses without requirement of the train dynamics model as long as some mild reasonable assumptions are satisfied, and the simulation research as well, the field experiments have not been carried out yet. In our future research, we will focus on this issue and address some other practical concerns, such as the operation-depended uncertainties and disturbances in train operations.

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Optimization of Train Regulation and Energy Usage of Metro Lines Using an Adaptive-Optimal-Control Algorithm

Cited by 69 publications

References 18 publications

Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Simulation Model

Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Simulation Model

An optimisation method for train scheduling with minimum energy consumption and travel time in metro rail systems

Coordinated Iterative Learning Control Schemes for Train Trajectory Tracking With Overspeed Protection

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