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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

机译:迭代学习和自适应容错控制,在未知速度延迟和控制输入饱和下应用于高速列车

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摘要

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 L2 [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.
机译:这项研究调查了具有致动器故障和未知的速度延迟以及控制输入饱和的高速列车的速度轨迹跟踪问题。无需精确的系统参数或执行器故障变量的分析估计边界,就可以得出新的自适应迭代学习容错控制(AILFTC)策略。结果表明,所提出的方法可以解决两种执行器故障,并且可以利用LyapunovΔKrasovskii函数分析未知的时变速度延迟和控制输入饱和度。这样,在不存在非运动场的情况下,在运行过程中,重复得到的控制算法能够在操作过程中反复实现列车速度达到所需曲线的L 2 [0,T] 收敛。理论分析和数值模拟验证了线性和参数不确定性。

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  • 来源
    《Control Theory & Applications, IET》 |2014年第9期|675-687|共13页
  • 作者

    Fan L.;

  • 作者单位

    Center for Intelligent Systems and Renewable Energy, Beijing Jiaotong University, People??s Republic of China|c|;

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  • 正文语种 eng
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