In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuous-time Markov jump piecewise-affne (PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed $H$ ∞ performance are demon-strated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating representation. Finally, two illustrative examples containing a class of tunnel diode circuit systems are presented to fully demonstrate the effectiveness and superiority of the proposed iterative learning observer with current feedback.

中文翻译：

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一类马尔可夫跳转分段仿射系统的故障估计：基于电流反馈的迭代学习方法

在本文中，研究了一类连续时间马尔可夫跳跃分段仿射（PWA）系统针对执行器和传感器故障的随机稳定性分析和故障估计问题。首先，设计了一种具有当前反馈的新型模式相关 PWA 迭代学习观测器，用于同时估计系统状态和故障，其中包含先前的迭代信息和当前的反馈机制。辅助反馈通道优化了观测器的响应速度，因此估计误差会迅速收敛到零。然后，保证随机稳定性的充分条件$H$ 无穷大 论证了估计误差系统的性能，并通过迭代累加表示建立了系统信息与估计信息之间的等价关系。最后，提出了包含一类隧道二极管电路系统的两个说明性示例，以充分证明所提出的具有电流反馈的迭代学习观测器的有效性和优越性。