Event-triggered state estimation for nonlinear systems aided by machine learning

Thời gian: 14:00 đến 16:00 Ngày 29/11/2021

Địa điểm: C101, VIASM

Báo cáo viên: Đinh Công Hướng - Quy Nhon University, Binh Dinh, Vietnam

Tóm tắt:

This paper considers the event-triggered state estimation problem with the aid of machine learning for nonlinear systems. First, we employ an ensemble of recurrent neural network (RNN) model to predict the nonlinear systems. Second, we design a discrete-time dynamic event-triggered mechanism and a state observer based on this mechanism for the prediction model. This discrete-time dynamic event-triggered state observer significantly reduces the utilization of communication resources. Third, we establish a sufficient condition to ensure that the state observer can robustly estimate the state vector of the RNN model. Finally, we provide an illustrative example to verify the merit of the proposed method.