Kybernetika 56 no. 1, 35-56, 2020

Optimized state estimation for nonlinear dynamical networks subject to fading measurements and stochastic coupling strength: An event-triggered communication mechanism

Chaoqing Jia, Jun Hu, Chongyang Lv and Yujing ShiDOI: 10.14736/kyb-2020-1-0035

Abstract:

This paper is concerned with the design of event-based state estimation algorithm for nonlinear complex networks with fading measurements and stochastic coupling strength. The event-based communication protocol is employed to save energy and enhance the network transmission efficiency, where the changeable event-triggered threshold is adopted to adjust the data transmission frequency. The phenomenon of fading measurements is described by a series of random variables obeying certain probability distribution. The aim of the paper is to propose a new recursive event-based state estimation strategy such that, for the admissible linearization error, fading measurements and stochastic coupling strength, a minimum upper bound of estimation error covariance is given by designing the estimator gain. Furthermore, the monotonicity relationship between the trace of the upper bound of estimation error covariance and the fading probability is pointed out from the theoretical aspect. Finally, a simulation example is used to show the effectiveness of developed state estimation algorithm.

Keywords:

event-based communication protocol, fading measurements, stochastic coupling strength, nonlinear dynamical networks, monotonicity analysis

Classification:

93C10, 93E03, 93E10

References:

  1. S. Boccaletti, V. Latora, Y. Moreno, M. Chavez and D.-U. Hwang: Complex networks: Structure and dynamics. Physics Reports 424 (2006), 4-5.   DOI:10.1016/j.physrep.2005.10.009
  2. G. Calafiore: Reliable localization using set-valued nonlinear filters. IEEE Trans. Systems Man Cybernet. Part A-Systems and Humans 35 (2005), 189-197.   DOI:10.1109/tsmca.2005.843383
  3. W. Chen, D. R. Ding, X. H. Ge, Q.-L. Han and G. L. Wei: $H_\infty$ containment control of multi-agent systems under event-triggered communication scheduling: The finite-horizon case. IEEE Trans. Cybernet. (2018), 1-11.   DOI:10.1109/tcyb.2018.2885567
  4. Y. G. Chen, S. M. Fei and Y. M. Li: Robust stabilization for uncertain saturated time-delay systems: a distributed-delay-dependent polytopic approach. IEEE Trans. Automat. Control 62 (2017), 3455-3460.   DOI:10.1109/tac.2016.2611559
  5. Y. G. Chen, Z. D. Wang, S. M. Fei and Q.-L. Han: Regional stabilization for discrete time-delay systems with actuator saturations via a delay-dependent polytopic approach. IEEE Trans. Automat. Control 64 (2019), 1257-1264.   DOI:10.1109/tac.2018.2847903
  6. D. R. Ding, Q.-L. Han, Z. D. Wang and X. H. Ge: A survey on model-based distributed control and filtering for industrial cyber-physical systems. IEEE Trans. Industr. Inform. 15 (2019), 2483-2499.   DOI:10.1109/tii.2019.2905295
  7. D. R. Ding, Z. D. Wang, Q.-L. Han and G. L. Wei: Neural-network-based output-feedback control under Round-Robin scheduling protocols. IEEE Trans. Cybernet. 49 (2019), 2372-2384.   DOI:10.1109/tcyb.2018.2827037
  8. X. H. Ge and Q.-L. Han: Consensus of multiagent systems subject to partially accessible and overlapping Markovian network topologies. IEEE Trans. Cybernet. 47 (2017), 1807-1819.   DOI:10.1109/tcyb.2016.2570860
  9. X. H. Ge, Q.-L. Han and Z. D. Wang: A threshold-parameter-dependent approach to designing distributed event-triggered $H_\infty$ consensus filters over sensor networks. IEEE Trans. Cybernet. 49 (2019), 1148-1159.   DOI:10.1109/tcyb.2017.2789296
  10. X. H. Ge, Q.-L. Han and Z. D. Wang: A dynamic event-triggered transmission scheme for distributed set-membership estimation over wireless sensor networks. IEEE Trans. Cybernet. 49 (2019), 171-183.   DOI:10.1109/tcyb.2017.2789296
  11. Z. Q. Geng, Z. Wang, H. X. Hu, Y. M. Han, X. Y. Lin and Y. H. Zhang: A fault detection method based on horizontal visibility graph-integrated complex networks: Application to complex chemical processes. Canad. J. Chemical Engrg. 97 (2019), 1129-1138.   DOI:10.1002/cjce.23319
  12. J. Hu, Z. D. Wang and H. J. Gao: Joint state and fault estimation for uncertain time-varying nonlinear systems with randomly occurring faults and sensor saturations. Automatica 97 (2018), 150-160.   DOI:10.1016/j.automatica.2018.07.027
  13. J. Hu, Z. D. Wang, G.-P. Liu and H. X. Zhang: Variance-constrained recursive state estimation for time-varying complex networks with quantized measurements and uncertain inner coupling. IEEE Trans. Neural Networks Learn. Systems (2019), 1-13.   DOI:10.1109/tnnls.2019.2927554
  14. J. Hu, Z. D. Wang, S. Liu and H. J. Gao: A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements. Automatica 64 (2016), 155-162.   DOI:10.1016/j.automatica.2015.11.008
  15. J. Hu, H. X. Zhang, X. Y. Yu, H. J. Liu and D. Y. Chen: Design of sliding-mode-based control for nonlinear systems with mixed-delays and packet losses under uncertain missing probability. IEEE Trans. Systems Man Cybernet.: Systems (2019), 1-12.   DOI:10.1109/tsmc.2019.2919513
  16. J. Hu, P. P. Zhang, Y. G. Kao, H. J. Liu and D. Y. Chen: Sliding mode control for Markovian jump repeated scalar nonlinear systems with packet dropouts: The uncertain occurrence probabilities case. Applied Math. Comput. 362 (2019), 124574.   DOI:10.1016/j.amc.2019.124574
  17. Y. F. Huang, S. Werner, J. Huang, N. Kashyap and V. Gupta: State estimation in electric power grids: Meeting new challenges presented by the requirements of the future grid. IEEE Signal Process. Magazine 29 (2012), 33-44.   DOI:10.1109/msp.2012.2187037
  18. J. Hu, Z. D. Wang, F. E. Alsaadi and T. Hayat: Event-based filtering for time-varying nonlinear systems subject to multiple missing measurements with uncertain missing probabilities. Inform. Fusion 38 (2017), 74-83.   DOI:10.1016/j.inffus.2017.03.003
  19. J. Hu, G.-P. Liu, H. X. Zhang and H. J. Liu: On state estimation for nonlinear dynamical networks with random sensor delays and coupling strength under event-based communication mechanism. Inform. Sci. 511 (2020), 265-283.   DOI:10.1016/j.ins.2019.09.050
  20. M. N. Kurt, Y. Yilmaz and X. D. Wang: Secure distributed dynamic state estimation in wide-area smart grids. IEEE Trans. Inform. Forensics Security 15 (2020) 800-815.   DOI:10.1109/tifs.2019.2928207
  21. J. J. Li, G. L. Wei, D. R. Ding and Y. R. Liu: Set-membership filtering for discrete time-varying nonlinear systems with censored measurements under Round-Robin protocol. Neurocomputing 281 (2018), 20-26.   DOI:10.1016/j.neucom.2017.11.033
  22. W. L. Li, Y. M. Jia and J. P. Du: Recursive state estimation for complex networks with random coupling strength. Neurocomputing 219 (2017), 1-8.   DOI:10.1016/j.neucom.2016.08.095
  23. W. L. Li, Y. M. Jia and J. P. Du: Distributed filtering for discrete-time linear systems with fading measurements and time-correlated noise. Digital Signal Process. 60 (2017), 211-219.   DOI:10.1016/j.dsp.2016.10.003
  24. X.-J. Li and G.-H. Yang: FLS-based adaptive synchronization control of complex dynamical networks with nonlinear couplings and state-dependent uncertainties. IEEE Trans. Cybernet. 46 (2018), 171-180.   DOI:10.1109/tcyb.2015.2399334
  25. X. X. Liu, X. J. Su, P. Shi, S. K. Nguang and C. Shen: Fault detection filtering for nonlinear switched systems via event-triggered communication approach. Automatica 101 (2019), 365-376.   DOI:10.1016/j.automatica.2018.12.006
  26. J. Manitz, J. Harbering, M. Schmidt, T. Kneib and A. Schobel: Source estimation for propagation processes on complex networks with an application to delays in public transportation systems. J. Royal Statist. Soc. Series C - Applied Statistics 66 (2017), 521-536.   DOI:10.1111/rssc.12176
  27. J. Y. Mao, D. R. Ding, Y. Song, Y. R. Liu and F. E. Alsaadi: Event-based recursive filtering for time-delayed stochastic nonlinear systems with missing measurements. Signal Process. 134 (2017), 158-165.   DOI:10.1016/j.sigpro.2016.12.004
  28. J. Y. Mao, D. R. Ding, G. L. Wei and H. J. Liu: Networked recursive filtering for time-delayed nonlinear stochastic systems with uniform quantisation under Round-Robin protocol. Int. J. Systems Sci. 50 (2019), 871-884.   DOI:10.1080/00207721.2019.1586002
  29. B. Shen, Z. D. Wang and H. Qiao: Event-triggered state estimation for discrete-time multidelayed neural networks with stochastic parameters and incomplete measurements. IEEE Trans. Neural Networks Learn. Systems 28 (2017), 1152-1163.   DOI:10.1109/tnnls.2016.2516030
  30. H. S. Shu, S. J. Zhang, B. Shen and Y. R. Liu: Unknown input and state estimation for linear discrete-time systems with missing measurements and correlated noises. Int. J. General Systems 45 (2016), 648-661.   DOI:10.1080/03081079.2015.1106732
  31. W. H. Song, J. A. Wang, C. Y. Wang and J. Y. Shan: A variance-constrained approach to event-triggered distributed extended Kalman filtering with multiple fading measurements. Int. J. Robust Nonlinear Control 29 (2019), 1558-1576.   DOI:10.1002/rnc.4456
  32. Y. C. Sun and G. H. Yang: Event-triggered state estimation for networked control systems with lossy network communication. Inform. Sci. 492 (2019), 1-12.   DOI:10.1016/j.ins.2019.03.058
  33. F. Wang, J. L. Liang, Z. D. Wang and X. H. Liu: A variance-constrained approach to recursive filtering for nonlinear 2-D systems with measurement degradations. IEEE Trans. Cybernetics 46 (2017), 1877-1887.   DOI:10.1109/tcyb.2017.2716400
  34. S. Y. Wang, X. G. Tian and H. J. Fang: Event-based state and fault estimation for nonlinear systems with logarithmic quantization and missing measurements. J. Franklin Inst. 356 (2019), 4076-4096.   DOI:10.1016/j.jfranklin.2018.11.044
  35. C. B. Wen, Z. D. Wang, Q. Y. Liu and F. E. Alsaadi: Recursive distributed filtering for a class of state-saturated systems with fading measurements and quantization effects. IEEE Trans. Systems Man Cybernet.: Systems 48 (2016), 930-941.   DOI:10.1109/tsmc.2016.2629464
  36. X. Wu, G. P. Jiang and X. W. Wang: State estimation for general complex dynamical networks with packet loss. IEEE Trans. Circuits Systems II: Express Briefs 65 (2017), 1753-1757.   DOI:10.1109/tcsii.2017.2767859
  37. Z.-G. Wu, Z. W. Xu, P. Shi, M. Z. Q. Chen and H. Y. Su: Nonfragile state estimation of quantized complex networks with switching topologies. IEEE Trans. Neural Networks Learn. Systems 29 (2018), 5111-5121.   DOI:10.1109/tnnls.2018.2790982
  38. L. Yan, S. J. Zhang, D. R. Ding, Y. R. Liu and F. E. Alsaadi: $H_\infty$ state estimation for memristive neural networks with multiple fading measurements. Neurocomputing 230 (2017), 23-29.   DOI:10.1016/j.neucom.2016.11.033
  39. H. X. Zhang, J. Hu, H. J. Liu, X. Y. Yu and F. Q. Liu: Recursive state estimation for time-varying complex networks subject to missing measurements and stochastic inner coupling under random access protocol. Neurocomputing 346 (2019), 48-57.   DOI:10.1016/j.neucom.2018.07.086
  40. H. X. Zhang, J. Hu, L. Zou, X. Y. Yu and Z. H. Wu: Event-based state estimation for time-varying stochastic coupling networks with missing measurements under uncertain occurrence probabilities. Int. J. General Systems 47 (2018), 506-521.   DOI:10.1080/03081079.2018.1445740
  41. X.-M. Zhang and Q.-L. Han: A decentralized event-triggered dissipative control scheme for systems with multiple sensors to sample the system outputs. IEEE Trans. Cybernet. 46 (2015), 2745-2757.   DOI:10.1109/tcyb.2015.2487420
  42. X.-M. Zhang, Q.-L. Han, X. H. Ge, D. R. Ding, L. Ding, D. Yue and C. Peng: Networked control systems: a survey of trends and techniques. IEEE/CAA J. Autom. Sinica (2019), 1-17.   DOI:10.1109/jas.2019.1911651
  43. Z. Y. Zuo, Q.-L. Han, B. D. Ning, X. H. Ge and X.-M. Zhang: An overview of recent advances in fixed-time cooperative control of multi-agent systems. IEEE Trans. Industr. Inform. 14 (2018), 2322-2334.   DOI:10.1109/tii.2018.2817248