模糊神经网络事件触发非脆弱H∞状态估计

doi:10.16182/j.issn1004731x.joss.201806042

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (6): 2335-2345.doi: 10.16182/j.issn1004731x.joss.201806042

模糊神经网络事件触发非脆弱H_∞状态估计

王艳芹^{1, 2}, 任伟建¹

1. 东北石油大学电气信息工程学院,大庆 163318;
2. 大庆师范学院机电工程学院,大庆 163712

收稿日期:2016-08-02 修回日期:2016-10-10 出版日期:2018-06-08 发布日期:2018-06-14
作者简介:王艳芹(1979-),女,黑龙江海伦,博士生,副教授,研究方向为非线性随机系统故障检测;任伟建(1963-),女,黑龙江泰来,博士,教授,研究方向为复杂系统建模与控制。
基金资助:
国家自然科学基金(61374127, 61422301)

Event-triggered Non-fragile H_∞ State Estimation for Fuzzy Time-Delay Neural Networks

Wang Yanqin^{1, 2}, Ren Weijian¹

1. College of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163318, China;
2. School of Mechanical and Electrical Engineering, Daqing Normal University, Daqing 163712, China

Received:2016-08-02 Revised:2016-10-10 Online:2018-06-08 Published:2018-06-14

摘要/Abstract

摘要： 针对一类受随机发生时变时滞和随机丢包现象影响的模糊时滞神经网络,设计事件触发机制下的非脆弱H_∞状态估计器。引入事件触发条件来判定信号是否传输,以减少网络资源占用率;采用高斯分布的随机变量和乘性增益不确定构造具有随机发生增益变化的非脆弱状态估计器。通过构造Lyapunov函数,利用随机运算、线性矩阵不等式技术,得出确保估计误差动态系统渐近稳定并满足H_∞性能约束的非脆弱性估计器存在的充分条件。求解该线性矩阵不等式后,得到估计器增益。最后,用仿真实例说明该状态估计器的有效性。

关键词: 状态估计, 非脆弱性, 时变时滞, 事件触发, 丢包

Abstract: For a class of fuzzy neural networks with randomly occurring time-varying delays and randomly data packet loss, an event-triggered non-fragile H_∞ state estimator is designed. The event-triggered condition is introduced to determine whether the signal is transmitted or not, so as to reduce the occupation rate of network resource. Random variables of Gaussian distribution and the multiplicative gain uncertainties are adopted to construct the non-fragile state estimator with randomly occurring gain variations. By constructing Lyapunov function, and via stochastic computation and linear matrix inequality technique, the sufficient conditions for the existence of non-fragile estimators are obtained, which guarantee the asymptotical stability and the H_∞ performance constraint of dynamic estimation error system. After solving the linear matrix inequality, the gains of the estimator are obtained. A simulation example is given to illustrate the feasibility of the state estimator.

Key words: state estimation, non-fragile, time-varying delays, event-triggered, packet dropouts

中图分类号:

TP273

王艳芹, 任伟建. 模糊神经网络事件触发非脆弱H_∞状态估计[J]. 系统仿真学报, 2018, 30(6): 2335-2345.

Wang Yanqin, Ren Weijian. Event-triggered Non-fragile H_∞ State Estimation for Fuzzy Time-Delay Neural Networks[J]. Journal of System Simulation, 2018, 30(6): 2335-2345.

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模糊神经网络事件触发非脆弱H_∞状态估计

Event-triggered Non-fragile H_∞ State Estimation for Fuzzy Time-Delay Neural Networks

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模糊神经网络事件触发非脆弱H∞状态估计

Event-triggered Non-fragile H∞ State Estimation for Fuzzy Time-Delay Neural Networks

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模糊神经网络事件触发非脆弱H_∞状态估计

Event-triggered Non-fragile H_∞ State Estimation for Fuzzy Time-Delay Neural Networks