Distributed resilient filtering of large-scale systems with channel scheduling

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.

[1]  Marcello Farina,et al.  Moving-horizon partition-based state estimation of large-scale systems , 2024, Autom..

[2]  Fuwen Yang,et al.  Event-Triggered Distributed Fusion Estimation of Networked Multisensor Systems With Limited Information , 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[3]  Qing-Long Han,et al.  Observer-Based Event-Triggered Control for Networked Linear Systems Subject to Denial-of-Service Attacks , 2020, IEEE Transactions on Cybernetics.

[4]  Qing-Long Han,et al.  A Set-Membership Approach to Event-Triggered Filtering for General Nonlinear Systems Over Sensor Networks , 2020, IEEE Transactions on Automatic Control.

[5]  Q. Han,et al.  Secure Distributed Finite-Time Filtering for Positive Systems Over Sensor Networks Under Deception Attacks , 2020, IEEE Transactions on Cybernetics.

[6]  Qing-Long Han,et al.  Networked control systems: a survey of trends and techniques , 2020, IEEE/CAA Journal of Automatica Sinica.

[7]  Seddik Bacha,et al.  Complex Power Electronics Systems Modeling and Analysis , 2019, IEEE Trans. Ind. Electron..

[8]  Qing-Long Han,et al.  Neural-Network-Based Output-Feedback Control Under Round-Robin Scheduling Protocols , 2019, IEEE Transactions on Cybernetics.

[9]  Wei Chen,et al.  Distributed Resilient Filtering for Power Systems Subject to Denial-of-Service Attacks , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[10]  Jizhen Liu,et al.  Model predictive control for load frequency of hybrid power system with wind power and thermal power , 2019, Energy.

[11]  Qing-Long Han,et al.  A Threshold-Parameter-Dependent Approach to Designing Distributed Event-Triggered $H_{\infty}$ Consensus Filters Over Sensor Networks , 2019, IEEE Transactions on Cybernetics.

[12]  Chao Yang,et al.  Distributed filtering under false data injection attacks , 2019, Autom..

[13]  Qing-Long Han,et al.  A Survey on Model-Based Distributed Control and Filtering for Industrial Cyber-Physical Systems , 2019, IEEE Transactions on Industrial Informatics.

[14]  Qing-Long Han,et al.  A Dynamic Event-Triggered Transmission Scheme for Distributed Set-Membership Estimation Over Wireless Sensor Networks , 2019, IEEE Transactions on Cybernetics.

[15]  Zidong Wang,et al.  Robust Kalman filtering for two-dimensional systems with multiplicative noises and measurement degradations: The finite-horizon case , 2018, Autom..

[16]  Le Yi Wang,et al.  Distributed Cooperative Optimal Control of DC Microgrids With Communication Delays , 2018, IEEE Transactions on Industrial Informatics.

[17]  Zhenhua Deng,et al.  Distributed event-triggered algorithm for optimal resource allocation of multi-agent systems , 2017, Kybernetika.

[18]  Jin Bae Park,et al.  Decentralized H∞ fuzzy filter for nonlinear large-scale sampled-data systems with uncertain interconnections , 2017, Fuzzy Sets Syst..

[19]  Qing-Long Han,et al.  Neuronal State Estimation for Neural Networks With Two Additive Time-Varying Delay Components , 2017, IEEE Transactions on Cybernetics.

[20]  Fuad E. Alsaadi,et al.  A Resilient Approach to Distributed Filter Design for Time-Varying Systems Under Stochastic Nonlinearities and Sensor Degradation , 2017, IEEE Transactions on Signal Processing.

[21]  Pengxiao Zhang,et al.  Event-triggered observer-based tracking control for leader-follower multi-agent systems , 2016, Kybernetika.

[22]  Jun Hu,et al.  A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements , 2016, Autom..

[23]  Wen-An Zhang,et al.  Energy Efficient Distributed Filtering for a Class of Nonlinear Systems in Sensor Networks , 2015, IEEE Sensors Journal.

[24]  M. Fu,et al.  Distributed weighted least-squares estimation with fast convergence for large-scale systems , 2015, Autom..

[25]  Subhrakanti Dey,et al.  Optimal Energy Allocation for Kalman Filtering Over Packet Dropping Links With Imperfect Acknowledgments and Energy Harvesting Constraints , 2014, IEEE Transactions on Automatic Control.

[26]  Wen-an Zhang,et al.  Distributed Finite-Horizon Fusion Kalman Filtering for Bandwidth and Energy Constrained Wireless Sensor Networks , 2014, IEEE Transactions on Signal Processing.

[27]  Aleksandar Haber,et al.  Moving Horizon Estimation for Large-Scale Interconnected Systems , 2013, IEEE Transactions on Automatic Control.

[28]  Michael Z. Q. Chen,et al.  Moving Horizon Estimation for Networked Systems With Quantized Measurements and Packet Dropouts , 2013, IEEE Transactions on Circuits and Systems I: Regular Papers.

[29]  Zidong Wang,et al.  Distributed H∞ state estimation with stochastic parameters and nonlinearities through sensor networks: The finite-horizon case , 2012, Autom..

[30]  Giancarlo Ferrari-Trecate,et al.  Hycon2 Benchmark: Power Network System , 2012, ArXiv.

[31]  Ling Shi,et al.  Sensor data scheduling for optimal state estimation with communication energy constraint , 2011, Autom..

[32]  Fabian R. Wirth,et al.  Small gain theorems for large scale systems and construction of ISS Lyapunov functions , 2009, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[33]  José M. F. Moura,et al.  Distributing the Kalman Filter for Large-Scale Systems , 2007, IEEE Transactions on Signal Processing.

[34]  Jianliang Wang,et al.  Robust nonfragile Kalman filtering for uncertain linear systems with estimator gain uncertainty , 2001, IEEE Trans. Autom. Control..

[35]  Derui Ding,et al.  ℋ∞ Containment Control of Multiagent Systems Under Event-Triggered Communication Scheduling: The Finite-Horizon Case , 2020, IEEE Trans. Cybern..

[36]  Guoqiang Hu,et al.  Distributed Kalman filtering for time-varying discrete sequential systems , 2019, Autom..

[37]  Ling Shi,et al.  Optimal sensor scheduling for multiple linear dynamical systems , 2017, Autom..

[38]  Wei Xiang,et al.  Microgrid State Estimation: A Distributed Approach , 2017 .

[39]  Jia Wang,et al.  Event-Triggered Generalized Dissipativity Filtering for Neural Networks With Time-Varying Delays , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[40]  Ruting Jia,et al.  Exponential H ∞ filter design for stochastic Markovian jump systems with both discrete and distributed time-varying delays , 2016 .

[41]  Lenka Pavelková,et al.  Nonlinear Bayesian state filtering with missing measurements and bounded noise and its application to vehicle position estimation , 2011, Kybernetika.