Fuzzy Fault Detection for Markov Jump Systems With Partly Accessible Hidden Information: An Event-Triggered Approach
针对一类非线性马尔可夫跳变系统，设计了一种事件触发的模糊异步故障检测滤波器，利用隐马尔可夫模型描述系统与滤波器间的异步现象，并通过轮式移动机械臂实例验证了该方法能准确检测故障且无误报。
This article addresses the design issue of fuzzy asynchronous fault detection filter (FAFDF) for a class of nonlinear Markov jump systems by an event-triggered (ET) scheme. The ET scheme can be applied to cut down the transmission times from the system to FAFDF. It is assumed that the system modes cannot be obtained synchronously by the filter, and instead, there is a detector that can measure the estimated modes of the system. The asynchronous phenomenon between the system and the filter is characterized via a hidden Markov model with partly accessible mode detection probabilities. Applying the Lyapunov function methods, sufficient conditions for the presence of FAFDF are obtained. Finally, an application of a wheeled mobile manipulator with hybrid joints is employed to clarify that the devised FAFDF can detect the faults without any incorrect alarm.