An iterative predictive learning control approach with application to train trajectory tracking

Sun, Heqing; Hou, Zhongsheng

doi:10.1109/ascc.2013.6606355

Cited by 9 publications

(17 citation statements)

References 22 publications

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“…As the train operates along a long distance and over long period, unnoticeable faults might occur in some of the traction and breaking units. In the works of [7,26], control input saturations have been taken into account in the control design. However, the unknown time-varying speed delays that may occur frequently during operation process have not been considered so far.…”

Section: Wwwietdlorgmentioning

confidence: 99%

“…For instance, in the work of [24], a novel ATO method is presented for high-speed train based on ILC. An iterative learning approach for train trajectory tracking control is proposed in [7]. For station control of the train, a terminal ILC scheme is introduced in [25].…”

Section: Wwwietdlorgmentioning

confidence: 99%

“…For instance, the traction/braking force is limited to the traction power and the actual speed of the train in practical operation. So the control input saturations are unavoidable during the train motion control [7,8]. Also, when the powering unit or braking unit (called 'actuator' hereafter) of train fail to work properly in that actuation/braking capabilities are fading, a FTC scheme should be designed to maintain safe operation and avoid any possible operation accident whenever a failure occurs [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Iterative learning and adaptive fault‐tolerant control with application to high‐speed trains under unknown speed delays and control input saturations

Fan

2014

IET Control Theory & Applications

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This study investigates the speed trajectory tracking problem of high-speed trains with actuator failures and unknown speed delays as well as control input saturations. New adaptive iterative learning fault-tolerant control (AILFTC) strategy is derived without the need for precise system parameters or analytically estimating bound on actuator failures variables. It is shown that with the proposed method, both actuator failures can be accommodated and the unknown time-varying speed delays and control input saturations can be analysed by means of Lyapunov-Krasovskii function. As such, the resultant control algorithms are able to achieve the L 2 [0,T ] convergence of the train speed to desired profile during operations repeatedly in the presence of non-linearities and parametric uncertainties, as validated by the theoretical analysis and numerical simulations.

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Section: Wwwietdlorgmentioning

confidence: 99%

Section: Wwwietdlorgmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Iterative learning and adaptive fault‐tolerant control with application to high‐speed trains under unknown speed delays and control input saturations

Fan

2014

IET Control Theory & Applications

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“…This feature makes it more suitable to address the train operation control problems. Some pioneer works have been done in this field, such as ILC-based train trajectory tracking control without considering overspeed protection and coordination [25] and terminal ILC-based station stop control [26].…”

Section: Introductionmentioning

confidence: 99%

“…In existing train operation algorithms [5]- [12], [25], overspeed protection and safe headway are not taken into account, and are left to be supervised by the separate ATP hardware system. Therefore, it is natural and necessary to introduce overspeed protection and safe headway control into train trajectory tracking algorithms to enhance safety.…”

Section: Introductionmentioning

confidence: 99%

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

Sun

Hou

2013

IEEE Trans. Automat. Sci. Eng.

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116

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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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Adaptive Iterative Learning Control Based High Speed Train Operation Tracking Under Iteration‐Varying Parameter and Measurement Noise

Li¹,

Hou²

2015

Asian Journal of Control

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The iterative learning control (ILC)-based automatic train operation is proposed to address a high speed train (HST) tracking problem with consideration of the iteration-varying operation condition. The iteration-varying operation condition considered in this paper is the air resistance coefficient of an HST, which may be completely different at two consecutive operation processes due to different weather conditions. In addition, to alleviate the effect of measurement noise, the proposed method is modified further. The effectiveness of these two proposed methods are verified by theoretical analysis and numerical simulation.

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An iterative predictive learning control approach with application to train trajectory tracking

Cited by 9 publications

References 22 publications

Iterative learning and adaptive fault‐tolerant control with application to high‐speed trains under unknown speed delays and control input saturations

Iterative learning and adaptive fault‐tolerant control with application to high‐speed trains under unknown speed delays and control input saturations

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

Adaptive Iterative Learning Control Based High Speed Train Operation Tracking Under Iteration‐Varying Parameter and Measurement Noise

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