Article
Full entry |
PDF
(0.8 MB)
Feedback
PDF
(0.8 MB)
Feedback
Keywords:
distributed filtering; sensor networks; non-Gaussian noises; network-induced phenomena; communication protocols
distributed filtering; sensor networks; non-Gaussian noises; network-induced phenomena; communication protocols
Summary:
Sensor networks are regarded as a promising technology in the field of information perception and processing owing to the ease of deployment, cost-effectiveness, flexibility, as well as reliability. The information exchange among sensors inevitably suffers from various network-induced phenomena caused by the limited resource utilization and complex application scenarios, and thus is required to be governed by suitable resource-saving communication mechanisms. It is also noteworthy that noises in system dynamics and sensor measurements are ubiquitous and in general unknown but can be bounded, rather than follow specific Gaussian distributions as assumed in Kalman-type filtering. Particular attention of this paper is paid to a survey of recent advances in distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks. First, two types of widely employed structures of distributed filters are reviewed, the corresponding analysis is systematically addressed, and some interesting results are provided. The inherent purpose of adding consensus terms into the distributed filters is profoundly disclosed. Then, some representative models characterizing various network-induced phenomena are reviewed and their corresponding analytical strategies are exhibited in detail. Furthermore, recent results on distributed filtering with non-Gaussian noises are sorted out in accordance with different network-induced phenomena and system models. Another emphasis is laid on recent developments of distributed filtering with various communication scheduling, which are summarized based on the inherent characteristics of their dynamic behavior associated with mathematical models. Finally, the state-of-the-art of distributed filtering and challenging issues, ranging from scalability, security to applications, are raised to guide possible future research.
References:
[1] Aazam, M., Zeadally, S., Harras, K. A.: Fog computing architecture, evaluation, and future research directions. IEEE Comm. Magazine 56, (2018), 5, 2018, 46-52. DOI 10.1109/mcom.2018.1700707 | MR 3843414
[2] Ahmad, F., Rasool, A., Ozsoy, E., Rajasekar, S., Sabanovic, A., Elitaş, M.: Distribution system state estimation-A step towards smart grid. Renewable Sustainable Energy Rev. 81 (2018), 2659-2671. DOI 10.1016/j.rser.2017.06.071
[3] Chen, W., Ding, D., Dong, H., Wei, G.: Distributed resilient filtering for power systems subject to denial-of-service attacks. IEEE Trans. Systems Man Cybernet.: Systems 49 (2019), 8, 1688-1697. DOI 10.1109/tsmc.2019.2905253
[4] Chen, W., Ding, D., Ge, X., Han, Q.-L., Wei, G.: $H_\infty$ containment control of multi-agent systems under event-triggered communication scheduling: The finite-horizon case. IEEE Trans. Cybernet. 50 (2020), 4, 1372-1382. DOI 10.1109/tcyb.2018.2885567
[5] Chen, Y., Wang, Z., Yuan, Y., Date, P.: Distributed $H_\infty$ filtering for switched stochastic delayed systems over sensor networks with fading measurements. IEEE Trans. Cybernet. 50 (2018), 1, 2-14. DOI 10.1109/tcyb.2018.2852290
[6] Ding, D., Han, Q.-L., Wang, Z., Ge, X.: Distributed recursive filtering of cyber-physical systems with security defenses. IEEE Trans. Systems Man Cybernet.: Systems. DOI 10.1109/tsmc.2019.2960541
[7] Ding, D., Han, Q.-L., Wang, Z., Ge, X.: A survey on model-based distributed control and filtering for industrial cyber-physical systems. IEEE Trans. Industr. Inform. 15 (2019), 5, 2483-2499. DOI 10.1109/tii.2019.2905295
[8] Ding, D., Wang, Z., Dong, H., Shu, H.: Distributed $H_\infty$ state estimation with stochastic parameters and nonlinearities through sensor networks: the finite-horizon case. Automatica 48 (2012), 8, 1575-1585. DOI 10.1016/j.automatica.2012.05.070 | MR 2950405
[9] Ding, D., Wang, Z., Han, Q.-L.: Neural-network-based output-feedback control with stochastic communication protocols. Automatica 106 (2019), 221-229. DOI 10.1016/j.automatica.2019.04.025 | MR 3952583
[10] Ding, D., Wang, Z., Han, Q.-L.: A set-membership approach to event-triggered filtering for general nonlinear systems over sensor networks. IEEE Trans. Automat. Control 65 (2020), 4, 1792-1799. DOI 10.1109/tac.2019.2934389 | MR 4052856
[11] Ding, D., Wang, Z., Han, Q.-L.: A scalable algorithm for event-triggered state estimation with unknown parameters and switching topologies over sensor networks. IEEE Trans. Cybernet. DOI 10.1109/tcyb.2019.2917543
[12] Ding, D., Wang, Z., Han, Q.-L., Wei, G.: Neural-network-based output-feedback control under Round-Robin scheduling protocols. IEEE Trans. Cybernet. 49 (2019), 6, 2372-2384. DOI 10.1109/tcyb.2018.2827037
[13] Ding, D., Wang, Z., Ho, D. W. C., Wei, G.: Distributed recursive filtering for stochastic systems under uniform quantizations and deception attacks through sensor networks. Automatica 78 (2017), 231-240. DOI 10.1016/j.automatica.2016.12.026 | MR 3614098
[14] Ding, D., Wang, Z., Lam, J., Shen, B.: Finite-Horizon $H_\infty$ control for discrete time-varying systems with randomly occurring nonlinearities and fading measurements. IEEE Trans. Automat. Control 60 (2015), 9, 2488-2493. DOI 10.1109/tac.2014.2380671 | MR 3393143
[15] Ding, D., Wang, Z., Shen, B., Shu, H.: $H_\infty$ state estimation for discrete-time complex networks with randomly occurring sensor saturations and randomly varying sensor delays. IEEE Trans. Neural Networks Learning Systems 23 (2012), 5, 725-736. DOI 10.1109/tnnls.2012.2187926
[16] Ding, L., Han, Q.-L., Zhang, X.-M.: Distributed secondary control for active power sharing and frequency regulation in islanded microgrids using an event-triggered communication mechanism. IEEE Trans. Industr. Inform. 15 (2019), 7, 3910-3922. DOI 10.1109/tii.2018.2884494
[17] Ding, L., Han, Q.-L., Ge, X., Zhang, X.-M.: An overview of recent advances in event-triggered consensus of multiagent systems. IEEE Trans. Cybernet. 48 (2018), 4, 1110-1123. DOI 10.1109/tcyb.2017.2771560 | MR 3554944
[18] Ding, L., Han, Q.-L., Wang, L., Sindi, E.: Distributed cooperative optimal control of DC microgrids with communication delays. IEEE Trans. Industr. Inform. 14 (2018), 9, 3924-3935. DOI 10.1109/tii.2018.2799239
[19] Dong, H., Wang, Z., Gao, H.: Distributed filtering for a class of time-varying systems over sensor networks with quantization errors and successive packet dropouts. IEEE Trans. Signal Process. 60 (2012), 6, 3164-3173. DOI 10.1109/tsp.2012.2190599 | MR 2924079
[20] Girard, A.: Dynamic triggering mechanisms for event-triggered control. IEEE Trans. Cybernet. 60 (2015), 7, 1992-1997. DOI 10.1109/tac.2014.2366855 | MR 3365092
[21] Ge, X., Han, Q.-L.: Distributed event-triggered $H_\infty$ filtering over sensor networks with communication delays. Inform. Sci. 291 (2015), 128-142. DOI 10.1016/j.ins.2014.08.047 | MR 3264405
[22] Ge, X., Han, Q.-L.: Distributed formation control of networked multi-agent systems using a dynamic event-triggered communication mechanism. IEEE Trans. Industr. Electron. 64 (2017), 10, 8118-8127. DOI 10.1109/tie.2017.2701778
[23] Ge, X., Han, Q.-L., Wang, Z.: A threshold-parameter-dependent approach to designing distributed event-triggered $H_\infty$ consensus filters over sensor networks. IEEE Trans. Cybernet. 49 (2019), 4, 1148-1159. DOI 10.1109/tcyb.2017.2789296
[24] Ge, X., Han, Q.-L., Wang, Z.: A dynamic event-triggered transmission scheme for distributed set-membership estimation over wireless sensor networks. IEEE Trans. Cybernet. 49 (2019), 1, 171-183. DOI 10.1109/tcyb.2017.2769722
[25] Ge, X., Han, Q.-L., Zhang, X.-M., Ding, L., Yang, F.: Distributed event-triggered estimation over sensor networks: A survey. IEEE Trans. Cybernet. 50 (2020), 3, 1306-1320. DOI 10.1109/tcyb.2019.2917179
[26] Ge, X., Han, Q.-L., Zhang, X.-M., Ding, D., Yang, F.: Resilient and secure remote monitoring for a class of cyber-physical systems against attacks. Inform. Sci. 512 (2020), 1592-1605. DOI 10.1016/j.ins.2019.10.057 | MR 4038642
[27] Ge, X., Han, Q.-L., Zhong, M., Zhang, X.-M.: Distributed Krein space-based attack detection over sensor networks under deception attacks. Automatica 109 (2019), 108557. DOI 10.1016/j.automatica.2019.108557 | MR 3998774
[28] Gupta, P., Kumar, P. R.: The capacity of wireless networks. IEEE Trans. Inform. Theory 46 (2000), 2, 388-404. DOI 10.1109/18.825799 | MR 1748976
[29] Han, F., Dong, H., Wang, Z., Li, G.: Local design of distributed $H_\infty$-consensus filtering over sensor networks under multiplicative noises and deception attacks. Int. J. Robust Nonlinear Control 29 (2019), 8, 2296-2314. DOI 10.1002/rnc.4493 | MR 3940120
[30] Han, F., Wei, G., Ding, D., Song, Y.: Local condition based consensus filtering with stochastic nonlinearities and multiple missing measurements. IEEE Trans. Automat. Control 62 (2017), 9, 4784-4790. DOI 10.1109/tac.2017.2689722 | MR 3691904
[31] Heemels, W. P. M. H., Johansson, K. H., Tabuada, P.: An introduction to eventtriggered and self-triggered control. In: Proc. 51st IEEE Conference on Decision and Control, Maui 2012, pp. 3270-3285. DOI 10.1109/cdc.2012.6425820 | MR 2952326
[32] Healy, M., Newe, T., Lewis, E.: Wireless sensor node hardware: A review. In: 2008 IEEE Sensor, Lecce 2008, pp. 621-624. DOI 10.1109/icsens.2008.4716517
[33] Hill, J. L., Culler, D. E.: Mica: a wireless platform for deeply embedded networks. IEEE Micro 22, (2002), 6, 12-24. DOI 10.1109/mm.2002.1134340
[34] Hu, J., Wang, Z., Liang, J., Dong, H.: Event-triggered distributed state estimation with randomly occurring uncertainties and nonlinearities over sensor networks: A delay-fractioning approach. J. Franklin Inst. 352 (2015), 3750-3763. DOI 10.1016/j.jfranklin.2014.12.006 | MR 3385893
[35] Hu, S., Yue, D., Chen, X., Cheng, Z., Xie, X.: Resilient $H_\infty$ filtering for event-triggered networked systems under nonperiodic DoS jamming attacks. IEEE Trans. Systems Man Cybernet.: Systems. DOI 10.1109/tsmc.2019.2896249
[36] Jenabzadeh, A., Safarinejadian, B.: A Lyapunov-based distributed consensus filter for a class of nonlinear stochastic systems. Automatica 86 (2017), 53-62. DOI 10.1016/j.automatica.2017.08.005 | MR 3711448
[37] Karray, F., Jmal, M. W., Garcia-Ortiz, A., Abid, M., Obeid, A. M.: A comprehensive survey on wireless sensor node hardware platforms. Comput. Networks 144, (2018), 89-110. DOI 10.1016/j.comnet.2018.05.010
[38] Li, J.-Y., Zhang, B., Lu, R., Xu, Y.: Robust distributed $H_\infty$ state estimation for stochastic periodic systems over constraint sensor networks. IEEE Trans. Systems Man Cybernet.: Systems. DOI 10.1109/tsmc.2018.2837047
[39] Li, Q., Shen, B., Wang, Z., Shen, W.: Recursive distributed filtering over sensor networks on Gilbert-Elliott channels: A dynamic event-triggered approach. Automatica 113 (2019), 108681. DOI 10.1016/j.automatica.2019.108681 | MR 4056010
[40] Li, Q., Shen, B., Wang, Z., Huang, T., Luo, J.: Synchronization control for a Class of discrete time-delay complex dynamical networks: A dynamic event-triggered approach. IEEE Trans. Cybernet. 49 (2019), 5, 1979-1986. DOI 10.1109/tcyb.2018.2818941 | MR 3891660
[41] Liang, J., Wang, Z., Liu, X.: Distributed state estimation for discrete-time sensor networks with randomly varying nonlinearities and missing measurements. IEEE Trans. Neural Networks 22 (2011), 3, 486-496. DOI 10.1109/tnn.2011.2105501
[42] Liu, D., Yang, G.-H.: Dynamic event-triggered control for linear time-invariant systems with $l_2$-gain performance. Int. J. Robust Nonlinear Control 29 (2019), 507-518. DOI 10.1002/rnc.4403 | MR 3890676
[43] Liu, J., Gu, Y., Cao, J., Fei, S.: Distributed event-triggered $H_\infty$ filtering over sensor networks with sensor saturations and cyber-attacks. ISA Trans. 81 (2018), 63-75. DOI 10.1016/j.isatra.2018.07.018
[44] Liu, K., Guo, H., Zhang, Q., Xia, Y.: Distributed secure filtering for discrete-time systems under Round-Robin protocol and deception attacks. IEEE Trans. Cybernet. DOI 10.1109/tcyb.2019.2897366
[45] Liu, Q., Wang, Z., He, X., Zhou, D. H.: Event-based distributed filtering with stochastic measurement fading. IEEE Trans. Industr. Inform. 11 (2015), 6, 1643-1652. DOI 10.1109/tii.2015.2444355 | MR 3671115
[46] Liu, S., Liu, P.: Distributed model-based control and scheduling for load frequency regulation of smart grids over limited bandwidth networks. IEEE Trans. Industr. Inform. 14 (2018), 5, 1814-1823. DOI 10.1109/tii.2017.2766666
[47] Liu, S., Wang, Z., Wei, G., Li, M.: Distributed set-membership filtering for multirate systems under the Round-Robin scheduling over sensor networks. IEEE Trans. Cybernetics. DOI 10.1109/tcyb.2018.2885653
[48] Liu, Y., Zhao, Y., Wu, F.: Ellipsoidal state-bounding-based set-membership estimation for linear system with unknown-but-bounded disturbances. IET Control Theory Appl. 10 (2016), 4, 431-442. DOI 10.1049/iet-cta.2015.0654 | MR 3495243
[49] Ma, L., Wang, Z., Han, Q.-L., Lam, H.-K.: Variance-constrained distributed filtering for time-varying systems with multiplicative noises and deception attacks over sensor networks. IEEE Sensors J. 17 (2017), 7, 2279-2288. DOI 10.1109/jsen.2017.2654325
[50] Ma, L., Wang, Z., Lam, H.-K., Kyriakoulis, N.: Distributed event-based set-membership filtering for a class of nonlinear systems with sensor saturations over sensor networks. IEEE Trans. Cybernet. 47 (2017), 11, 3772-3783. DOI 10.1109/tcyb.2016.2582081
[51] Mahmud, R., Toosi, A. N., Ramamohanarao, K., Buyya, R.: Context-aware placement of industry 4.0 applications in fog computing environments. IEEE Trans. Industr. Inform. DOI 10.1109/tii.2019.2952412
[52] Marin-Perianu, M., Meratnia, N., Havinga, P., et.al.: Decentralized enterprise systems: a multiplatform wireless sensor network approach. IEEE Wireless Commun. 14, (2007), 6, 57-66. DOI 10.1109/mwc.2007.4407228
[53] Meral, M., Çelík, D.: A comprehensive survey on control strategies of distributed generation power systems under normal and abnormal conditions. Ann. Rev. Control 47 (2019), 112-132. DOI 10.1016/j.arcontrol.2018.11.003 | MR 3973204
[54] Mihai, V., Dragana, C., Stamatescu, G., Popescu, D., Ichim, L.: Wireless sensor network architecture based on fog computing. In: 5th International Conference on Control, Decision and Information Technologies. Thessaloniki, 2018, pp. 743-747. DOI 10.1109/codit.2018.8394851
[55] Millán, P., Orihuela, L., Vivas, C., Rubio, F.: Distributed consensus-based estimation considering network induced delays and dropouts. Automatica 48 (2012), 10, 2726-2729. DOI 10.1016/j.automatica.2012.06.093 | MR 2961178
[56] Olfati-Saber, R.: Distributed Kalman filtering for sensor networks. In: Proc. 46th IEEE Conference on Decision and Control, New Orleans 2007, pp. 5492-5498. DOI 10.1109/cdc.2007.4434303
[57] Olfati-Saber, R.: Kalman-consensus filter: Optimality, stability, and performance. In: Proc. 48h IEEE Conference on Decision and Control, Shanghai 2009, pp. 7036-7042. DOI 10.1109/cdc.2009.5399678
[58] Olfati-Saber, R., Jalalkamali, P.: Coupled distributed estimation and control for mobile sensor networks. IEEE Trans. Automat. Control 57 (2012), 10, 2609-2614. DOI 10.1109/tac.2012.2190184 | MR 2991662
[59] Rafi, A., Rehman, A., Ali, G., Akram, J.: Efficient energy utilization in fog computing based wireless sensor networks. In: 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur 2019, pp. 1-5. DOI 10.1109/icomet.2019.8673423
[60] Rahman, T., Yao, X., Tao, G., Ning, H., Zhou, Z.: Efficient edge nodes reconfiguration and selection for the internet of things. IEEE Sensors J. 19, (2019), 12, 4672-4679. DOI 10.1109/jsen.2019.2895119
[61] Satyanarayanan, M., Schuster, R., Ebling, M., Fettweis, G., Flinck, H., Joshi, K., Sabnani, K.: An open ecosystem for mobile-cloud convergence. IEEE Commun. Magazine 53, (2015), 3, 63-70. DOI 10.1109/mcom.2015.7060484
[62] Sarkar, S., Wankar, R., Srirama, S., Suryadevara, N. K.: Serverless management of sensing systems for fog computing framework. IEEE Sensors J. 20 (2020), 3, 1564-1572. DOI 10.1109/jsen.2019.2939182
[63] Shen, B., Wang, Z., Hung, Y. S.: Distributed $H_\infty$-consensus filtering in sensor networks with multiple missing measurements: The finite-horizon case. Automatica 66 (2010), 10, 1682-1688. DOI 10.1016/j.automatica.2010.06.025 | MR 2877323 | Zbl 1204.93122
[64] Shen, B., Wang, Z., Liu, X.: A stochastic sampled-data approach to distributed $H_\infty$ filtering in sensor networks. IEEE Trans. Circuits Systems I: Regular Papers 58 (2011), 9, 2237-2246. DOI 10.1109/tcsi.2011.2112594 | MR 2868162
[65] Shen, B., Wang, Z., Qiao, H.: Event-triggered state estimation for discrete-time multidelayed neural networks with stochastic parameters and incomplete measurements. IEEE Trans. Neural Networks Learning Systems 28 (2017), 5, 1152-1163. DOI 10.1109/tnnls.2016.2516030 | MR 3721783
[66] Song, H., Yu, L., Zhang, W.-A.: Multi-sensor-based $H_\infty$ estimation in heterogeneous sensor networks with stochastic competitive transmission and random sensor failures. IET Control Theory Appl. 8 (2014), 3, 202-210. DOI 10.1049/iet-cta.2013.0432 | MR 3185345
[67] Souravlias, D., Parsopoulos, K.: Particle swarm optimization with neighborhood-based budget allocation. Int. J. Machine Learning Cybernet. 7 (2016), 3, 451-477. DOI 10.1007/s13042-014-0308-3
[68] Su, H., Li, Z., Ye, Y.: Event-triggered Kalman-consensus filter for two-target tracking sensor networks. ISA Trans. 71 (2017), 1, 103-111. DOI 10.1016/j.isatra.2017.06.019 | MR 3468618
[69] Su, X., Wu, L., Shi, P.: Sensor networks with random link failures: Distributed filtering for T-S fuzzy systems. IEEE Trans. Industr. Inform. 9 (2013), 3, 1739-1750. DOI 10.1109/tii.2012.2231085
[70] Sun, Z., Wei, L., Xu, C., Wang, T., Nie, Y., Xing, X., Lu, J.: An energy-efficient cross-layer-sensing clustering method based on intelligent fog computing in WSNs. IEEE Access 14, (2019), 7, 144165-144177. DOI 10.1109/access.2019.2944858
[71] Tan, Y., Xiong, M., Niu, B., Liu, J., Fei, S.: Distributed hybrid-triggered $H_\infty$ filter design for sensor networked systems with output saturations. Neurocomputing 315 (2018), 261-271. DOI 10.1016/j.neucom.2018.07.022
[72] Ugrinovskii, V.: Distributed robust filtering with $H_\infty$ consensus of estimates. Automatica 47 (2011), 1, 1-13. DOI 10.1016/j.automatica.2010.10.002 | MR 2878241 | Zbl 1209.93152
[73] Ugrinovskii, V., Fridman, E.: A Round-Robin type protocol for distributed estimation with $H_\infty$ consensus. Systems Control Lett. 69 (2014), 103-110. DOI 10.1016/j.sysconle.2014.05.001 | MR 3212828 | Zbl 1288.93009
[74] Ugrinovskii, V.: Distributed $H_\infty$ estimation resilient to biasing attacks. IEEE Trans. Control Network Systems 7 (2020), 1, 458-470. DOI 10.1109/tcns.2019.2924192
[75] Wan, X., Wang, Z., Han, Q.-L., Wu, M.: Finite-time $H_\infty$ state estimation for discrete time-delayed genetic regulatory networks under stochastic communication protocols. IEEE Trans. Circuits Systems I: Regular Papers 65 (2018), 10, 3481-3491. DOI 10.1109/tcns.2019.2924192 | MR 3854691
[76] Wan, X., Wang, Z., Wu, M., Liu, X.: $H_\infty$ state estimation for discrete-time nonlinear singularly perturbed complex networks under the Round-Robin protocol. IEEE Trans. Neural Networks Learning Systems 30 (2019), 2, 415-426. DOI 10.1109/tnnls.2018.2839020 | MR 3914858
[77] Wang, D., Wang, Z., Li, G., Wang, W.: Distributed filtering for switched nonlinear positive systems with missing measurements over sensor networks. IEEE Sensors J. 16 (2016), 12, 4940-4948. DOI 10.1109/jsen.2016.2555761
[78] Wang, D., Wang, Z., Shen, B., Li, Q.: $H_\infty$ finite-horizon filtering for complex networks with state saturations: The weighted try-once-discard protocol. Int. J. Robust Nonlinear Control 29 (2019), 2096-2111. DOI 10.1002/rnc.4479 | MR 3940107
[79] Wang, L., Wang, Z., Han, Q.-L., Wei, G.: Event-based variance-constrained $H_\infty$ filtering for stochastic parameter systems over sensor networks with successive missing measurements. IEEE Trans. Cybernet. 48 (2018), 3, 1007-1017. DOI 10.1109/tcyb.2017.2671032 | MR 1988100
[80] Wang, T., Qiu, J., Fu, S., Ji, W.: Distributed fuzzy $H_\infty$ filtering for nonlinear multirate networked double-layer industrial processes. IEEE Trans. Industr. Electron. 64 (2017), 6, 5203-5211. DOI 10.1109/tie.2016.2622234
[81] Wang, X.-L., Yang, G.-H.: Distributed event-triggered $H_\infty$ filtering for discrete-time T-S fuzzy systems over sensor networks. IEEE Trans. Systems Man Cybernet.: Systems. DOI 10.1109/tsmc.2018.2882540
[82] Wen, C., Wang, Z., Liu, Q., Alsaadi, F. E.: Recursive distributed filtering for a class of state-saturated systems with fading measurements and quantization effects. IEEE Trans. Systems Man Cybernet.: Systems 48 (2018), 6, 930-941. DOI 10.1109/tsmc.2016.2629464
[83] Xiao, S., Han, Q.-L., Ge, X., Zhang, Y.: Secure distributed finite-time filtering for positive systems over sensor networks under deception attacks. IEEE Trans. Cybernet. 50 (2020), 3, 1220-1229. DOI 10.1109/tcyb.2019.2900478
[84] Xu, Y., Lu, R., Shi, P., Li, H., Xie, S.: Finite-time distributed state estimation over sensor networks with Round-Robin protocol and fading channels. IEEE Trans. Cybernet. 48 (2018), 1, 336-345. DOI 10.1109/tcyb.2016.2635122
[85] Yan, H., Yang, Q., Zhang, H., Yang, F., Zhan, X.: Distributed $H_\infty$ state estimation for a class of filtering networks with time-varying switching topologies and packet losses. IEEE Trans. Systems Man Cybernet.: Systems 48 (2018), 12, 2047-2057. DOI 10.1109/tsmc.2017.2708507
[86] Yan, H., Zhang, H., Yang, F., Huang, C., Chen, S.: Distributed $H_\infty$ filtering for switched repeated scalar nonlinear systems with randomly occurred sensor nonlinearities and asynchronous switching. IEEE Trans. Systems Man Cybernet.: Systems 48 (2018), 12, 2263-2270. DOI 10.1109/tsmc.2017.2754495
[87] Yang, F., Han, Q.-L., Liu, Y.: Distributed $H_\infty$ state estimation over a filtering network with time-varying and switching topology and partial information exchange. IEEE Trans. Cybernet. 49 (2019), 3, 870-882. DOI 10.1109/tcyb.2017.2789212
[88] Yang, F., Xia, N., Han, Q.-L.: Event-based networked islanding detection for distributed solar PV generation systems. IEEE Trans. Industr. Inform. 13 (2017), 1, 322-329. DOI 10.1109/tii.2016.2607999
[89] Yang, W., Wang, X. F., Shi, H. B.: Optimal consensus-based distributed estimation with intermittent communication. Int. J. Systems Sci. 42 (2011), 9, 1521-1529. DOI 10.1080/00207721.2011.565135 | MR 2819529
[90] Yin, X., Li, Z., Zhang, L., Han, M.: Distributed state estimation of sensor-network systems subject to Markovian channel switching with application to a chemical process. IEEE Trans. Systems Man Cybernet.: Systems 48 (2018), 6, 864-874. DOI 10.1109/tsmc.2016.2632155
[91] Yu, H., Zhuang, Y., Wang, W.: Distributed $H_\infty$ filtering in sensor networks with randomly occurred missing measurements and communication link failures. Inform. Sci. 222 (2013), 424-438. DOI 10.1016/j.ins.2012.07.059 | MR 2998522
[92] Yu, W., Deng, Z., Zhou, H., Zeng, X.: Distributed event-triggered algorithm for optimal resource allocation of multi-agent systems. Kybernetika 53 (2017), 5, 747-764. DOI 10.14736/kyb-2017-5-0747 | MR 3750101
[93] Yu, Y., Shen, Y.: Robust sampled-data observer design for lipschitz nonlinear systems. Kybernetika 54 (2018), 4, 699-717. DOI 10.14736/kyb-2018-4-0699 | MR 3863251
[94] Zhang, D., Shi, P., Zhang, W.-A., Yu, L.: Energy-efficient distributed filtering in sensor networks: A unified switched system approach. IEEE Trans. Cybernet. 46 (2017), 7, 1618-1629. DOI 10.1109/tcyb.2016.2553043 | MR 3537173
[95] Zhang, D., Yu, L., Zhang, W.-A.: Energy efficient distributed filtering for a class of nonlinear systems in sensor networks. IEEE Sensors J. 15 (2015), 5, 3026-3036. DOI 10.1109/jsen.2014.2386348
[96] Zhang, H., Hong, Q., Yan, H., Yang, F., Guo, G.: Event-based distributed $H_\infty$ filtering networks of 2-DOF quarter-car suspension systems. IEEE Trans. Industr. Inform. 13 (2017), 1, 312-321. DOI 10.1109/tii.2016.2569566
[97] Zhang, H., Wang, Z., Yan, H., Yang, F., Zhou, X.: Adaptive event-triggered transmission scheme and $H_\infty$ filtering co-design over a filtering network with switching topology. IEEE Trans. Cybernet. 49 (2019), 12, 4296-4307. DOI 10.1109/tcyb.2018.2862828 | MR 3957647
[98] Zhang, L., Ning, Z., Wang, Z.: Distributed filtering for fuzzy time-delay systems with packet dropouts and redundant channels. IEEE Trans. Systems Man Cybernet.: Systems 46 (2016), 6, 559-572. DOI 10.1109/tsmc.2015.2435700
[99] Zhang, P., Wang, J.: Event-triggered observer-based tracking control for leader-follower multi-agent systems. Kybernetika 52 (2016), 4, 589-606. DOI 10.14736/kyb-2016-4-0589 | MR 3565771
[100] Zhang, W.-A., Dong, H., Guo, G., Yu, L.: Distributed sampled-data $H_\infty$ filtering for sensor networks with nonuniform sampling periods. IEEE Trans. Industr. Inform. 10 (2014), 2, 871-881. DOI 10.1109/tii.2014.2299897
[101] Zhang, X.-M., Han, Q.-L.: State estimation for static neural networks with time-varying delays based on an improved reciprocally convex inequality. IEEE Trans. Neural Networks Learning Syst. 313 (2018), 29, 1376-1381. DOI 10.1109/tnnls.2017.2661862 | MR 3867869
[102] Zhang, X.-M., Han, Q.-L., Ge, X., Ding, D.: An overview of recent developments in Lyapunov-Krasovskii functionals and stability criteria for recurrent neural networks with time-varying delays. Neurocomputing 313 (2018), 392-401. DOI 10.1016/j.neucom.2018.06.038
[103] Zhang, X.-M., Han, Q.-L., Ge, X., Ding, D., Ding, L., Yue, D., Peng, C.: Networked control systems: a survey of trends and techniques. IEEE/CAA J. Automat. Sinica 7 (2020), 1, 1-17. DOI 10.1109/jas.2019.1911651 | MR 3841465
[104] Zhang, X.-M., Han, Q.-L., Seuret, A., Gouaisbaut, F., He, Y.: Overview of recent advances in stability of linear systems with time-varying delays. IET Control Theory Appl. 13 (2019), 1, 1-16. DOI 10.1049/iet-cta.2018.5188 | MR 3888201
[105] Zhang, X.-M., Han, Q.-L., Ge, X.: Novel stability criteria for linear time-delay systems using Lyapunov-Krasovskii functionals with a cubic polynomial on time-varying delay. IEEE/CAA J. Automat. Sinica. DOI 10.1109/jas.2020.1003111
[106] Zhu, S., Chen, C., Li, W., Yang, B., Guan, X.: Distributed optimal consensus filter for target tracking in heterogeneous sensor networks. IEEE Trans. Cybernet. 43 (2013), 6, 1963-1976. DOI 10.1109/tsmcb.2012.2236647
[107] Zhu, Y., Zhang, L., Zheng, W.: Distributed $H_\infty$ filtering for a class of discrete-time Markov jump Lur'e systems with redundant channels. IEEE Trans. Industr. Electron. 63 (2016), 3, 1876-1885. DOI 10.1109/tie.2015.2499169
[108] Zou, L., Wang, Z., Han, Q.-L., Zhou, D.: Ultimate boundedness control for networked systems with try-once-discard protocol and uniform quantization effects. IEEE Trans. Automat. Control 62 (2017), 12, 6582-6588. DOI 10.1109/tac.2017.2713353 | MR 3743543
[109] Zou, L., Wang, Z., Han, Q.-L., Zhou, D.: Recursive filtering for time-varying systems with random access protocol. IEEE Trans. Automat. Control 64 (2019), 2, 720-727. DOI 10.1109/tac.2017.2713353 | MR 3912120
[110] Zou, L., Wang, Z., Han, Q.-L., Zhou, D.: Moving horizon estimation for networked time-delay systems under Round-Robin protocol. IEEE Trans. Automat. Control 64 (2019), 12, 5191-5198. DOI 10.1109/tac.2019.2910167 | MR 4044317
[111] Zou, L., Wang, Z., Han, Q.-L., Zhou, D.: Full information estimation for linear time-varying systems with Round-Robin protocol: A recursive filter approach. IEEE Trans. Systems Man Cybernet.: Systems. DOI 10.1109/tac.2018.2833154
Search
Partner of
