Distributed resilient filtering of large-scale systems with channel scheduling

Xu, Lili ; Zhang, Sunjie ; Wang, Licheng

Kybernetika, Tome 56 (2020) no. 1, pp. 170-188

Abstract (VO)

This paper addresses the distributed resilient filtering for discrete-time large-scale systems (LSSs) with energy constraints, where their information are collected by sensor networks with a same topology structure. As a typical model of information physics systems, LSSs have an inherent merit of modeling wide area power systems, automation processes and so forth. In this paper, two kinds of channels are employed to implement the information transmission in order to extend the service time of sensor nodes powered by energy-limited batteries. Specifically, the one has the merit of high reliability by sacrificing energy cost and the other reduces the energy cost but could result in packet loss. Furthermore, a communication scheduling matrix is introduced to govern the information transmission in these two kind of channels. In this scenario, a novel distributed filter is designed by fusing the compensated neighboring estimation. Then, two matrix-valued functions are derived to obtain the bounds of the covariance matrices of one-step prediction errors and the filtering errors. In what follows, the desired gain matrices are analytically designed to minimize the provided bounds with the help of the gradient-based approach and the mathematical induction. Furthermore, the effect on filtering performance from packet loss is profoundly discussed and it is claimed that the filtering performance becomes better when the probability of packet loss decreases. Finally, a simulation example on wide area power systems is exploited to check the usefulness of the designed distributed filter.

Détail

DOI : 10.14736/kyb-2020-1-0170

Classification : 93A15, 93C55
Keywords: distributed filtering; large-scale systems; energy constraints; sensor networks; power systems

Bibliographie

[1] Bacha, S., Li, H., Montenegro-Martinez, D.: Complex power electronics systems modeling and analysis. IEEE Trans. Industr. Electron. 66 (2019), 8, 6412-6415. | DOI

[2] Chen, W., Ding, D., Dong, H., Wei, G.: Distributed resilient filtering for power systems subject to deial-of-service attacks. IEEE Trans. Systems Man Cybernet.: Systems. 49 (2019), 8, 1688-1697. | DOI

[3] 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. (2018), 1-11.

[4] Chen, B., Hu, G., Ho, Daniel W. C., Yu, L.: Distributed Kalman filtering for time-varying discrete sequential systems. Automatica 99 (2019), 228-236. | DOI | MR

[5] Chen, B., Zhang, W. A., Yu, L.: Distributed finite-horizon fusion Kalman filtering for bandwidth and energy constrained wireless sensor networks. IEEE Trans. Signal Process. 62 (2014), 4, 797-812. | DOI | MR

[6] Dashkovskiy, S. N., Rüffer, B. S., Wirth, F. R.: Small gain theorems for large scale systems and construction of ISS Lyapunov functions. SIAM J. Control Optim. 48 (2010), 6, 4089-4118. | DOI | MR

[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

[8] 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

[9] 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 | MR

[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 (2019), 1-1. | DOI

[11] 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

[12] Farina, M., Ferrari-Trecate, G., Scattolini, R.: Moving-horizon partition-based state estimation of large-scale systems. Automatica 46 (2010), 910-918. | DOI | MR

[13] 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

[14] 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

[15] Haber, A., Verhaegen, M.: Moving horizon estimation for large-scale interconnected systems. IEEE Trans. Automat. Control 58 (2013), 11, 2834-2847. | DOI | MR

[16] Han, D., Wu, J., Zhang, H.: Optimal sensor scheduling for multiple linear dynamical systems. Automatica 75 (2017), 260-270. | DOI | MR

[17] Hu, S., Yue, D., Han, Q.-L.: Observer-based event-triggered control for networked linear systems subject to denial-of-service attacks. IEEE Trans. Cybernet. (2019), 1-13. | DOI | MR

[18] Hu, J., Wang, Z., Liu, S., Gao, H.: A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements. Automatica 64 (2016), 155-162. | DOI | MR

[19] Khan, U. A., Moura, J. M. F.: Distributing the Kalman filter for large-scale systems. IEEE Trans. Signal Process. 56 (2008), 10, 4919-4935. | DOI | MR

[20] Kim, H., Park, J., Joo, Y.: Decentralized $H_\infty$ fuzzy filter for nonlinear large-scale sampled-data systems with uncertain interconnections. Fuzzy Sets Systems 344 (2018), 145-162. | DOI | MR

[21] Liang, J., Wang, F., Wang, Z.: Robust Kalman filtering for two-dimensional systems with multiplicative noises and measurement degradations: The finite-horizon case. Automatica 96 (2018), 166-177. | DOI | MR

[22] Liu, Q., Wang, Z., He, X.: A resilient approach to distributed filter design for time-varying systems under stochastic nonlinearities and sensor degradation. IEEE Trans. Signal Process. 65 (2017), 5, 1300-1309. | DOI | MR

[23] Liu, J., Yao, Q., Hu, Y.: Model predictive control for load frequency of hybrid power system with wind power and thermal power. Energy 172 (2019), 555-565. | DOI | MR

[24] Liu, A., Yu, L., Zhang, W.-A., Chen, M. Z. Q.: Moving horizon estimation for networked systems with quantized measurements and packet dropouts. IEEE Trans. Circuits Systems I: Regular Papers 60 (2013), 7, 1823-1834. | DOI | MR

[25] Ma, L., Xu, M., Jia, R., Ye, H.: Exponential $H_\infty$ filter design for stochastic Markovian jump systems with both discrete and distributed time-varying delays. Kybernetika 50 (2014),4, 491-511. | DOI | MR

[26] Marelli, D., Fu, M.: Distributed weighted least-squares estimation with fast convergence for large-scale systems. Automatica 51 (2015) 27-39. | DOI | MR

[27] Nourian, M., Leong, A., Dey, S.: Optimal energy allocation for Kalman filtering over packet dropping links with imperfect acknowledgments and energy harvesting constraints. IEEE Trans. Automat. Control 59 (2014), 8, 2128-2143. | DOI | MR

[28] Pavelkova, L.: Nonlinear Bayesian state filtering with missing measurements and bounded noise and its application to vehicle position estimation. Kybernetika 47 (2011), 3, 370-384. | MR

[29] Rana, M., Li, L., Su, S. W: Microgrid state estimation: A distributed approach. IEEE Trans.Ind. Inform. 14 (2018), 8, 3368-3375. | DOI

[30] Riverso, S., Trecate, G. F.: Hycon2 benchmark: Power network system. arXiv: 1207.2000vl (2012).

[31] Shi, L., Cheng, P., Chen, J.: Sensor data scheduling for optimal state estimation with communication energy constraint. Automatica 47 (2011), 8, 1693-1698. | DOI | MR

[32] Wang, J., Zhang, X.-M., Han, Q.-L.: Event-triggered generalized dissipativity filtering for neural networks with time-varying delays. IEEE Trans. Neural Networks Learning Systems 27 (2016), 1, 77-88. | DOI | MR

[33] 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 (2019), 3, 1220-1229. | DOI

[34] Yan, H., Li, P., Zhang, H., Zhan, X., Yang, F.: Event-triggered distributed fusion estimation of networked multisensor systems with limited information. IEEE Trans. Systems Man Cybernet.: Systems (2018), 1-8.

[35] Yang, G. H., Wang, J. L.: Robust nonfragile Kalman filtering for uncertain linear systems with estimator gain uncertainty. IEEE Trans. Automat. Control 46 (2001), 2, 343-348. | DOI | MR

[36] Yang, W., Zhang, Y., Chen, G.: Distributed filtering under false data injection attacks. Automatica 102 (2019), 4, 34-44. | DOI | MR

[37] 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 | MR

[38] Zhang, X.-M., Han, Q.-L., Peng, C.: Networked control systems: a survey of trends and techniques. IEEE/CAA J. Autom. Sinica (2019), 1-17. | DOI | MR

[39] Zhang, X.-M., Han, Q.-L., Wang, Z. D., Zhang, B.-L.: Neuronal state estimation for neural networks with two additive time-varying delay components. IEEE Trans. Cybernet. 47 (2017), 10, 3184-3194. | DOI | MR

[40] Zhang, P., Wang, J.: Event-triggered observer-based tracking control for leader-follower multi-agent systems. Kybernetika 52 (2016),4, 589-606. | DOI | MR

[41] 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