An Iterative Learning Approach for Train Trajectory Tracking Control1

Sun, Heqing; Hou, Zhongsheng; Tang, Tao

doi:10.3182/20110828-6-it-1002.01270

Cited by 15 publications

(14 citation statements)

References 15 publications

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“…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%

“…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, 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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“…Traction controllers are used to controlling a vehicle under adverse external conditions [1]. In the railway vehicle, increasing the slip ratio decreases the friction between the rail and the wheel, and consequently, it reduces the tractive effort, which is not desired [2][3]. In the railway, slip occurs when the friction between the rail and wheel is low and, the peripheral speed of the wheel is more than the longitudinal velocity of the rail vehicle [2].…”

Section: Introductionmentioning

confidence: 99%

“…Using a traction control system, the slip ratio keeps in an optimal amount, improving the tractive effort which causes a reduction in both energy consumption and maintenance costs. By maximizing the tractive force between the wheels and rail, a traction controller prevents the wheel from slipping [2][3]. The induction motors have been widely applied as the traction motor because of the low maintenance, low cost [4].…”

Section: Introductionmentioning

confidence: 99%

Black-box Identification and Validation of an Induction Motor in an Experimental Application

Fathabadi¹,

Molavi²

2019

EJEE

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This article presents a simple approach to identify an induction motor model which is used in a traction control system for the purpose of slip prevention. In this study, an identified model has been extracted by real and identified parameters of the induction motor in the MATLAB, Simulink; as the purpose of this study is to identify a linear model for the induction motor, the speed control loops have been opened, therefore d-q voltages have been designed as the exiting inputs. After several considerations and examination, ARMAX model has been selected as a mathematical model in the identification process, then the open loop identified model has been replaced in the closed-loop structure, and by using the real data measurements, obtained from the closed-loop system in the Advanced Motion Control Laboratory, the open loop extracted model is verified.

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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 Learning Approach for Train Trajectory Tracking Control1

Cited by 15 publications

References 15 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

Black-box Identification and Validation of an Induction Motor in an Experimental Application

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

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