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Virtual Parameter Learning-Based Adaptive Control for Protective Automatic Train Operation
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DOI：10.1109/TITS.2021.3066447.png)
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Abstract
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This article addresses the speed-distance trajectory tracking control problem for railway trains to facilitate the effectuation of automation train operation (ATO). By proposing a new virtual parameter learning-based approach, we develop an adaptive control that exhibits twofold new features with comparison to the existing literatures: i), while the nonlinear operational resistance and railway line gradient profile are unknown, the proposed control not only bears a quite computationally inexpensive simplicity in structure but also achieves accurate tracking control with respect to the speed-distance trajectory benefiting by the virtual parameter learning approach and requiring no function approximators with linearized structure, for example, common utilization of neural or fuzzy approximations, to cope with the uncertain dynamic nonlinearities in real-time, and ii), by introducing a nonlinear error transformation, the protection enveloping problem, which is generally introduced by the onboard automatic train protection and wayside subsystems, operating independently from the ATO subsystem in practice, are considered explicitly to the control design for ATO for the first time. By invoking Lyapunov stability theorem, the resulting closed-loop system is guaranteed to be globally stable with rigorously analysis and proof. Meanwhile, in order to verify and validate the effectiveness and advantages of theoretical findings, experimental and comparative results, by applying the designed controller to the whole Beijing railway Yizhuang line, are shown.
Keywords:
Rail transportation
Adaptive control
Resistance
Mathematical model
Target tracking
Real-time systems
Uncertainty
Protective automatic train operation
virtual parameter learning
learning based adaptive control
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