Neural Network-Based State Estimation for Nonlinear Systems with Denial-of-Service Attack Under Try-Once-Discard Protocol

Abstract

Dear Editor, This letter deals with state estimation issues of discrete-time non-linear systems subject to denial-of-service (DoS) attacks under the try-once-discard (TOD) protocol. More specifically, to reduce the communication burden, a TOD protocol with novel update rules on protocol weights is designed for scheduling measurement outputs. In addition, unknown nonlinear functions vulnerable to DoS attacks are considered due to the openness and vulnerability of the network. For such systems, the neural networks (NNs) are exploited to estimate the unknown nonlinear system dynamics in the designed Luenberger-like observer. With the help of Lyapunov theory, some sufficient conditions are derived under which the estimation error and the approximation errors of NNs weights are uniformly ultimately bounded (UUB). Finally, the validity of designed observers is demonstrated by a power system example.