Distributed H∞-Consensus Fault Detection for Fuzzy Systems with Faults, Switching Network Topology, Packet Dropouts and Channel Fading

This paper is concerned with the distributed H∞-consensus fault detection (FD) filtering problem for a class of discrete-time Takagi-Sugeno (T-S) fuzzy systems with faults, switching network topology, channel fading and different communication channels-induced packet dropouts with different missing rates. The purpose of the addressed problem is to design a distributed H∞-consensus FD filter to guarantee the sensitivity of the residual signal to the faults and the robustness of the residual system to effects of both switching network topology, channel fading and different communication channels-induced packet dropouts with different lossing rates. On the basis of the T-S fuzzy approach and the Lyapunov functional, distributed H∞-consensus FD filter design criterion is derived such that the residual system is exponentially stable in the mean square, and the optimal H∞ filtering performance index is derived. A simulation example is conducted to verify the usefulness of the proposed FD filter design approach.

[1]  Steven X. Ding,et al.  Real-Time Implementation of Fault-Tolerant Control Systems With Performance Optimization , 2014, IEEE Transactions on Industrial Electronics.

[2]  S. Sitharama Iyengar,et al.  Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks , 2004, IEEE Transactions on Computers.

[3]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[4]  Marcel Staroswiecki,et al.  H ∞ fault detection filter design for linear discrete-time systems with multiple time delays , 2003, Int. J. Syst. Sci..

[5]  Hongjiu Yang,et al.  Fault Detection for T-S Fuzzy Discrete Systems in Finite-Frequency Domain. , 2011, IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society.

[6]  Fuwen Yang,et al.  H∞ filtering for nonlinear networked systems with randomly occurring distributed delays, missing measurements and sensor saturation , 2016, Inf. Sci..

[7]  M. Potkonjak,et al.  On-line fault detection of sensor measurements , 2003, Proceedings of IEEE Sensors 2003 (IEEE Cat. No.03CH37498).

[8]  Huijun Gao,et al.  On H-infinity Estimation of Randomly Occurring Faults for A Class of Nonlinear Time-Varying Systems With Fading Channels , 2016, IEEE Transactions on Automatic Control.

[9]  Hamid Gharavi,et al.  Special issue on sensor networks and applications , 2003 .

[10]  Ping Li,et al.  Distributed weighting fault-tolerant algorithm for event region detection in wireless sensor networks , 2008, 2008 International Conference on Communications, Circuits and Systems.

[11]  R. Olfati-Saber,et al.  Distributed Kalman Filter with Embedded Consensus Filters , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[12]  Valery A. Ugrinovskii,et al.  Distributed robust estimation over randomly switching networks using H∞ consensus , 2015, Autom..

[13]  Ligang Wu,et al.  Sensor Networks With Random Link Failures: Distributed Filtering for T–S Fuzzy Systems , 2013, IEEE Transactions on Industrial Informatics.