Dynamic event-triggered mechanism for H∞ non-fragile state estimation of complex networks under randomly occurring sensor saturations

Abstract In this paper, the problem of non-fragile H∞ state estimation is investigated for a class of discrete-time complex networks subject to randomly occurring sensor saturations (ROSSs) under a dynamic event-triggered mechanism (DETM). The ROSS phenomenon is taken into account in the network measurements as a reflection of the probabilistic limitation of the physical sensors, and the DETM is implemented to govern the signal transmission from the sensor to its corresponding state estimator. The objective of the problem addressed is to design an H∞ non-fragile state estimator under the DETM that can tolerate the possible gain perturbations, thereby possessing the desired non-fragility. By constructing a novel Lyapunov function, a sufficient condition is established such that the estimation error dynamics is exponentially mean-square stable with a prescribed H∞ performance level, and then the estimator gains are parameterized according to certain matrix inequalities. A simulation example is provided to demonstrate the effectiveness of the proposed state estimation scheme.

[1]  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.

[2]  J. Sola,et al.  Importance of input data normalization for the application of neural networks to complex industrial problems , 1997 .

[3]  Jie Cao,et al.  CAMAS: A cluster-aware multiagent system for attributed graph clustering , 2017, Inf. Fusion.

[4]  A. Barabasi,et al.  Bose-Einstein condensation in complex networks. , 2000, Physical review letters.

[5]  Xiuying Li,et al.  $H_\infty $ H ∞ control for networked stochastic non-linear systems with randomly occurring sensor saturations, multiple delays and packet dropouts , 2017 .

[6]  Domingo Giménez,et al.  Parameterized Schemes of Metaheuristics: Basic Ideas and Applications With Genetic Algorithms, Scatter Search, and GRASP , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[7]  Fuad E. Alsaadi,et al.  Variance-constrained H∞ state estimation for time-varying multi-rate systems with redundant channels: The finite-horizon case , 2019, Inf. Sci..

[8]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[9]  Qing-Long Han,et al.  A Recursive Approach to Quantized ${H_{\infty}}$ State Estimation for Genetic Regulatory Networks Under Stochastic Communication Protocols , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Emilia Fridman,et al.  Event-Triggered $H_{\infty}$ Control: A Switching Approach , 2015, IEEE Transactions on Automatic Control.

[11]  Fuwen Yang,et al.  H∞ filtering for nonlinear networked systems with randomly occurring distributed delays, missing measurements and sensor saturation , 2016, Inf. Sci..

[12]  Magdi S. Mahmoud,et al.  Resilient linear filtering of uncertain systems , 2004, Autom..

[13]  Q. Han,et al.  Event‐triggered H∞ control for a class of nonlinear networked control systems using novel integral inequalities , 2017 .

[14]  Yurong Liu,et al.  Distributed $H_\infty$ State Estimation Over a Filtering Network With Time-Varying and Switching Topology and Partial Information Exchange , 2019, IEEE Transactions on Cybernetics.

[15]  Qing-Long Han,et al.  Finite-Time $H_{\infty}$ State Estimation for Discrete Time-Delayed Genetic Regulatory Networks Under Stochastic Communication Protocols , 2018, IEEE Transactions on Circuits and Systems I: Regular Papers.

[16]  Yong He,et al.  Delay-dependent criteria for robust stability of time-varying delay systems , 2004, Autom..

[17]  Wenling Li,et al.  Variance-Constrained State Estimation for Nonlinearly Coupled Complex Networks , 2018, IEEE Transactions on Cybernetics.

[18]  Daniel E. Quevedo,et al.  On the Trade-Off Between Communication and Control Cost in Event-Triggered Dead-Beat Control , 2017, IEEE Transactions on Automatic Control.

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

[20]  Yurong Liu,et al.  Distributed filtering for nonlinear time‐delay systems over sensor networks subject to multiplicative link noises and switching topology , 2019, International Journal of Robust and Nonlinear Control.

[21]  Raquel Caballero-Águila,et al.  Distributed fusion filters from uncertain measured outputs in sensor networks with random packet losses , 2017, Inf. Fusion.

[22]  Li Sheng,et al.  Distributed resilient filtering for time-varying systems over sensor networks subject to Round-Robin/stochastic protocol. , 2019, ISA transactions.

[23]  Fuad E. Alsaadi,et al.  Finite-Time State Estimation for Recurrent Delayed Neural Networks With Component-Based Event-Triggering Protocol , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[24]  Dong Yue,et al.  Adaptive event‐triggered control for nonlinear discrete‐time systems , 2016 .

[25]  Dan Zhang,et al.  Non-fragile distributed filtering for fuzzy systems with multiplicative gain variation , 2016, Signal Process..

[26]  Qing-Long Han,et al.  Regional Stabilization for Discrete Time-Delay Systems With Actuator Saturations via A Delay-Dependent Polytopic Approach , 2019, IEEE Transactions on Automatic Control.

[27]  Fuad E. Alsaadi,et al.  Design of non-fragile state estimators for discrete time-delayed neural networks with parameter uncertainties , 2016, Neurocomputing.

[28]  Antoine Girard,et al.  Dynamic Triggering Mechanisms for Event-Triggered Control , 2013, IEEE Transactions on Automatic Control.

[29]  Min Wu,et al.  State Estimation for Discrete Time-Delayed Genetic Regulatory Networks With Stochastic Noises Under the Round-Robin Protocols , 2018, IEEE Transactions on NanoBioscience.

[30]  Tingwen Huang,et al.  Synchronization Control for A Class of Discrete Time-Delay Complex Dynamical Networks: A Dynamic Event-Triggered Approach , 2019, IEEE Transactions on Cybernetics.

[31]  Samarjit Kar,et al.  Coevolution of cooperation and network structure in social dilemmas in evolutionary dynamic complex network , 2018, Appl. Math. Comput..

[32]  Yonggang Chen,et al.  Exponential Synchronization for Delayed Dynamical Networks via Intermittent Control: Dealing With Actuator Saturations , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[33]  Fuad E. Alsaadi,et al.  A Partial-Nodes-Based Information fusion approach to state estimation for discrete-Time delayed stochastic complex networks , 2019, Inf. Fusion.

[34]  Zidong Wang,et al.  $H_{\infty}$ State Estimation for Discrete-Time Nonlinear Singularly Perturbed Complex Networks Under the Round-Robin Protocol , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[35]  Dong Yue,et al.  A Delay System Method for Designing Event-Triggered Controllers of Networked Control Systems , 2013, IEEE Transactions on Automatic Control.

[36]  Dan Zhang,et al.  Asynchronous and Resilient Filtering for Markovian Jump Neural Networks Subject to Extended Dissipativity , 2019, IEEE Transactions on Cybernetics.

[37]  Fan Wang,et al.  Resilient filtering for time-varying stochastic coupling networks under the event-triggering scheduling , 2018, Int. J. Gen. Syst..

[38]  Yan Song,et al.  Finite-horizon bounded H ∞ synchronisation and state estimation for discrete-time complex networks: local performance analysis , 2017 .

[39]  Wei Xing Zheng,et al.  Dynamic Event-Based Control of Nonlinear Stochastic Systems , 2017, IEEE Transactions on Automatic Control.

[40]  Yuxin Zhao,et al.  Resilient Asynchronous $H_{\infty }$ Filtering for Markov Jump Neural Networks With Unideal Measurements and Multiplicative Noises , 2015, IEEE Transactions on Cybernetics.

[41]  Fuad E. Alsaadi,et al.  Recursive state estimation based-on the outputs of partial nodes for discrete-time stochastic complex networks with switched topology , 2018, J. Frankl. Inst..