Proportional-Integral Observer-Based State Estimation for Singularly Perturbed Complex Networks With Cyberattacks

This article investigates the asynchronous proportional-integral observer (PIO) design issue for singularly perturbed complex networks (SPCNs) subject to cyberattacks. The switching topology of SPCNs is regulated by a nonhomogeneous Markov switching process, whose time-varying transition probabilities are polytope structured. Besides, the multiple scalar Winner processes are applied to character the stochastic disturbances of the inner linking strengths. Two mutually independent Bernoulli stochastic variables are exploited to characterize the random occurrences of cyberattacks. In a practical viewpoint, by resorting to the hidden nonhomogeneous Markov model, an asynchronous PIO is formulated. Under such a framework, by applying the Lyapunov theory, sufficient conditions are established such that the augmented dynamic is mean-square exponentially ultimately bounded. Finally, the effectiveness of the theoretical results is verified by two numerical simulations.

Automated summary

This article investigates the design issue of asynchronous proportional-integral observer (PIO) for singularly perturbed complex networks subject to cyberattacks. The stochastic disturbances of the inner linking strengths are characterized using a nonhomogeneous Markov switching process and multiple scalar Winner processes. By utilizing the Lyapunov theory, sufficient conditions are established to ensure that the augmented dynamics are mean-square exponentially ultimately bounded.