Dynamic‐event‐based state estimation of complex networks against attacks and innovation outliers: The probability constraint case

Abstract

AbstractThis article is devoted to the state estimation issue under probability constraint for a class of time‐varying stochastic complex networks with uncertain inner couplings and cyber‐attacks. To save the communication energy, the dynamic event‐triggered strategy is deployed in each individual sensor‐to‐estimator channel to govern the data transmissions. A multi‐attack model incorporating two different deception signals is considered to describe the phenomenon of randomly occurring cyber‐attacks. The saturation‐type function is involved to reduce the possible innovation outliers resulting from the hostile deception signals. A sufficient condition is first developed to guarantee the probability performance constraint on estimation error dynamics over certain finite horizon, and then the suitable state estimator gains are derived by means of a set of matrix inequalities. Subsequently, a recursive algorithm is put forward for the addressed finite‐horizon state estimator design problem. Finally, the validity of the proposed state estimation scheme is testified by a numerical example.