Fault Detection for Fuzzy Systems With Intermittent Measurements
Fault Detection for Fuzzy Systems With Intermittent Measurements
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DOI：
10.1109/tfuzz.2009.2014860
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发表时间：
2009-04
影响因子：
11.9
通讯作者：
Yan Zhao;J. Lam;Huijun Gao
中科院分区：
文献类型：
--
作者：
Yan Zhao;J. Lam;Huijun Gao
研究具有间歇量测的Takagi-Sugeno(T-S)模糊系统的故障检测问题。假定对象和故障检测过滤器之间的通信链路是不完美的(即，间歇性地发生数据分组丢失，这通常出现在网络环境中)，并且利用满足伯努利随机二进制分布的随机变量来对不可靠的通信链路建模。其目的是设计一种模糊故障检测滤波器，使得对于所有数据缺失情况，残差系统是随机稳定的，并且保持保证的性能。问题的求解采用依赖于基的Lyapunov函数方法，该方法比二次方法保守性小。所得结果也推广到具有时变参数不确定性的T-S模糊系统。所有的结果都以线性矩阵不等式的形式表示，可以通过标准的数值软件很容易地求解。给出了两个算例，说明了所发展的理论结果的实用性和适用性。
This paper investigates the problem of fault detection for Takagi-Sugeno (T-S) fuzzy systems with intermittent measurements. The communication links between the plant and the fault detection filter are assumed to be imperfect (i.e., data packet dropouts occur intermittently, which appear typically in a network environment), and a stochastic variable satisfying the Bernoulli random binary distribution is utilized to model the unreliable communication links. The aim is to design a fuzzy fault detection filter such that, for all data missing conditions, the residual system is stochastically stable and preserves a guaranteed performance. The problem is solved through a basis-dependent Lyapunov function method, which is less conservative than the quadratic approach. The results are also extended to T--S fuzzy systems with time-varying parameter uncertainties. All the results are formulated in the form of linear matrix inequalities, which can be readily solved via standard numerical software. Two examples are provided to illustrate the usefulness and applicability of the developed theoretical results.