IEEE Trans Cybern. 2023 Mar;53(3):1712-1724. doi: 10.1109/TCYB.2021.3108773. Epub 2023 Feb 15.
This article addresses the distributed multiple fault isolation, modeling, and the closed-loop fault estimation under asynchronous switching for high speed train (HST) with switched dynamics, which is composed of traction, coasting, and braking. First, directed-graph-quantum-learning-based multiple-agent system (MAS) classifiers are introduced to characterize the joints effects of multiple faults. Some sufficient conditions are derived under the condition that the multiple fault topology contains a directed spanning tree and cycle edge, and these conditions guarantee that the multiple fault isolation problem can be solved under randomized learning techniques. Then, single-integrator agents are employed to capture the time-varying topology of multiple fault modeling, in which edge agreement and persistence condition are used to guarantee asymptotic consensus. After that, a novel robust fault estimation design along with the switched Lyapunov function and average dwell time is proposed for the possible power actuator faults subject to asynchronous switching and electromagnetic interferences. In addition, switched estimators are designed such that the closed-loop system is asymptotically stable. A multiple fault isolation and estimation case is investigated to validate the application of this methodology.
本文针对高速列车（HST）切换动态的分布式多故障隔离、建模和闭环故障估计进行了研究，高速列车由牵引、滑行和制动组成。首先，引入基于有向图量子学习的多智能体系统（MAS）分类器来描述多故障的联合效应。在多故障拓扑包含有向生成树和循环边的条件下，得出了一些充分条件，这些条件保证了在随机学习技术下可以解决多故障隔离问题。然后，采用单积分器代理来捕获多故障建模的时变拓扑，其中使用边一致性和持续条件来保证渐近一致性。之后，针对可能存在异步切换和电磁干扰的功率执行器故障，提出了一种新的鲁棒故障估计设计，结合切换李雅普诺夫函数和平均驻留时间。此外，设计了切换估计器，使得闭环系统渐近稳定。研究了一个多故障隔离和估计案例，以验证该方法的应用。
