Fuzzy-model-based fault detection for nonlinear networked control systems with periodic access constraints and Bernoulli packet dropouts

Abstract In this paper, the problem of fault detection for nonlinear networked control systems subject to both communication constraints and packet dropouts is investigated. Despite the huge bulk of studies in the area of linear networked control systems, nonlinear networked control systems have been less involved. In the proposed method, Takagi–Sugeno fuzzy model is employed to represent nonlinear networked control systems. To model the communication network, access constraints and packet dropouts are respectively considered as periodic sequences and Bernoulli processes. Based on the optimal periodic residual generator theory, a fuzzy-model-based observer is proposed to deal with the problem of fault detection for nonlinear networked control systems. Both residual evaluation and correspondence threshold are also investigated to generate confident fault alarms. Finally, the effectiveness of the proposed fault detection scheme for nonlinear networked control systems is illustrated and some simulation results are carried out to validate the proposed approach.

[1]  Xin-Ping Guan,et al.  Neural network observer-based networked control for a class of nonlinear systems , 2014, Neurocomputing.

[2]  S. Ding,et al.  Fault detection design of networked control systems , 2011 .

[3]  Huajing Fang,et al.  Observer-based fault detection for networked discrete-time infinite-distributed delay systems with packet dropouts , 2012 .

[4]  Qing-Long Han,et al.  Modelling and controller design for discrete-time networked control systems with limited channels and data drift , 2014, Inf. Sci..

[5]  S. Bittanti,et al.  Analysis of discrete-time linear periodic systems , 1996 .

[6]  Zhangqing Zhu,et al.  Fault detection for nonlinear networked control systems based on fuzzy observer , 2012 .

[7]  Huajing Fang,et al.  Takagi-sugeno fuzzy-model-based fault detection for networked control systems with Markov delays , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[8]  Qingling Zhang,et al.  Simultaneous H2/H∞ fault detection and control for networked systems with application to forging equipment , 2016, Signal Process..

[9]  Guoping Lu,et al.  Fault detection for state-delay fuzzy systems subject to random communication delay , 2012 .

[10]  Bor-Sen Chen,et al.  Robust Fuzzy Observer-Based Fuzzy Control Design for Nonlinear Discrete-Time Systems With Persistent Bounded Disturbances , 2009, IEEE Transactions on Fuzzy Systems.

[11]  Dan Ye,et al.  Transmission-Dependent Fault Detection and Isolation Strategy for Networked Systems Under Finite Capacity Channels , 2017, IEEE Transactions on Cybernetics.

[12]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .

[13]  Qing-Long Han,et al.  Fault detection filter design for data reconstruction-based continuous-time networked control systems , 2016, Inf. Sci..

[14]  Shengyuan Xu,et al.  Fault detection for a class of nonlinear networked control systems with Markov sensors assignment and random transmission delays , 2014, J. Frankl. Inst..

[15]  Huajing Fang,et al.  Fault detection for networked systems subject to access constraints and packet dropouts , 2011 .

[16]  Cheng Huang,et al.  Fault detection observer design for networked control system with long time-delays and data packet dropout , 2010 .

[17]  Derong Liu,et al.  Fault detection and control co-design for discrete-time delayed fuzzy networked control systems subject to quantization and multiple packet dropouts , 2017, Fuzzy Sets Syst..

[18]  Wei Zhang,et al.  Stability of networked control systems , 2001 .

[19]  Guang-Hong Yang,et al.  Fault detection in finite frequency domain for networked control systems with missing measurements , 2013, J. Frankl. Inst..

[20]  Lei Zhang,et al.  Communication and control co-design for networked control systems , 2006, Autom..

[21]  Dawei Zhang,et al.  Networked fuzzy output feedback control for discrete-time Takagi-Sugeno fuzzy systems with sensor saturation and measurement noise , 2018, Inf. Sci..

[22]  Yongqiang Wang,et al.  Fault detection of NCS based on eigendecomposition, adaptive evaluation and adaptive threshold , 2007, Int. J. Control.

[23]  Y. Chu,et al.  Fault detection for a class of non-linear networked control systems in the presence of Markov sensors assignment with partially known transition probabilities , 2015 .

[24]  Steven X. Ding,et al.  Fault Detection of Networked Control Systems Subject to Access Constraints and Random Packet Dropout , 2009 .

[25]  Qingling Zhang,et al.  Fault detection for stochastic parameter-varying Markovian jump systems with application to networked control systems , 2016 .

[26]  Huajing Fang,et al.  H∞ fault detection for nonlinear networked systems with multiple channels data transmission pattern , 2013, Inf. Sci..

[27]  Sing Kiong Nguang,et al.  Robust fault estimator design for uncertain networked control systems with random time delays: An ILMI approach , 2010, Inf. Sci..

[28]  Jitesh H. Panchal,et al.  Decentralized Control Framework and Stability Analysis for Networked Control Systems , 2015 .

[29]  Yuanqing Xia,et al.  A network-bound-dependent stabilization method of networked control systems , 2013, Autom..

[30]  Huajing Fang,et al.  H∞-based fault detection for nonlinear networked systems with random packet dropout and probabilistic interval delay , 2011 .

[31]  Peng Shi,et al.  Fault detection for networked control systems with quantization and Markovian packet dropouts , 2015, Signal Process..

[32]  G.-Y. Tang,et al.  Fault diagnosis for networked control systems with delayed measurements and inputs , 2010 .

[33]  Qing Zhao,et al.  Closed-loop design of fault detection for networked non-linear systems with mixed delays and packet losses , 2013 .

[34]  Zehui Mao,et al.  H/sub /spl infin// fault detection filter design for networked control systems modelled by discrete Markovian jump systems , 2007 .