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Robust estimation and fault detection and isolation algorithms for stochastic linear hybrid systems with unknown fault input

机译：具有未知故障输入的随机线性混合系统的鲁棒估计，故障检测和隔离算法

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摘要

In this study, we develop algorithms for robust estimation and fault detection and identification for a class of hybrid systems called the stochastic linear hybrid system (SLHS). The authors propose a robust hybrid estimation algorithm that estimates the continuous state and the discrete state of an SLHS with unknown fault inputs. The algorithm decouples the unknown fault input from the estimation error dynamics for each discrete state of the hybrid system to guarantee the convergence of the estimation error. The robust hybrid estimation algorithm is designed for two kinds of discrete state transition models: the Markov-jump transition model whose discrete transition probabilities are constant (i.e. independent of the continuous state) and the state-dependent transition model whose discrete state transitions are determined by some guard conditions (i.e. dependent on the continuous state). The proposed residual generation algorithm computes residuals to facilitate fault detection and isolation. The residuals have the properties that they can reconstruct (in the mean sense) the unknown fault input vector. The authors also demonstrate the performance of the proposed algorithm with a vertical take-off and landing aircraft example.
机译：在这项研究中，我们为一类称为随机线性混合系统（SLHS）的混合系统开发了用于鲁棒估计，故障检测和识别的算法。作者提出了一种鲁棒的混合估计算法，该算法可估计具有未知故障输入的SLHS的连续状态和离散状态。对于混合系统的每个离散状态，该算法将未知故障输入与估计误差动态解耦，以确保估计误差的收敛性。针对两种离散状态转移模型设计了鲁棒的混合估计算法：离散转移概率恒定（即独立于连续状态）的马尔可夫跳跃模型和离散状态转移由以下公式确定的状态相关转移模型：一些保护条件（即取决于连续状态）。提出的残差生成算法计算残差以促进故障检测和隔离。残差具有可以重构（平均意义上）未知故障输入向量的属性。作者还以垂直起降飞机为例，演示了该算法的性能。

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  • 来源
    《Control Theory & Applications, IET》 |2011年第12期|p.1353-1368|共16页
  • 作者

    Liu W.; Hwang I.;

  • 作者单位

    School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN 47907, USA;

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