Dynamic Event-Triggered Approach for Distributed State and Parameter Estimation Over Networks Subjected to Deception Attacks

This study is focused on addressing the dynamic event-triggered distributed state and unknown parameter estimation problem for discrete-time nonlinear systems that have known linear dynamics and unknown nonlinearities and are subject to deception attacks. A neural-network-based unified estimation framework is introduced to estimate the unknown nonlinear function in conjunction with the system state and unknown parameters. Each sensor uses its own measurements and data from the neighboring sensors to calculate the overall estimates. The information-sharing network is assumed to be vulnerable to deception attacks, which are modeled using a Bernoulli distributed random variable. Additionally, a dynamic event-triggered strategy is adopted to alleviate resource consumption. Based on Lyapunov theory, the stability of the unified estimation framework is proven in terms of the uniformly ultimately bounded error. Moreover, the design conditions for the estimator are presented in the form of matrix inequalities. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed framework.