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Event-triggered state estimation for nonlinear systems aid by machine learning |
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| Authors: | D C Huong T N Nguyen H T Le H Trinh |
| Affiliation: | 1. The Faculty of Automotive Engineering Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam;2. Department of Mathematics and Statistics, Quy Nhon University, Binh Dinh, Vietnam
Contribution: ?Investigation, Methodology, Software, Supervision, Validation, Writing - original draft;3. Department of Mathematics and Statistics, Quy Nhon University, Binh Dinh, Vietnam Contribution: Conceptualization, Formal analysis, ?Investigation, Methodology, Supervision, Validation, Visualization;4. School of Engineering, Deakin University, Geelong, VIC, Australia Contribution: Conceptualization, Data curation, Formal analysis, Funding acquisition, ?Investigation, Methodology, Resources, Software, Supervision, Visualization |
| Abstract: | The event-triggered state estimation problem with the aid of machine learning for nonlinear systems is considered in this paper. First, we develop a recurrent neural network (RNN) model to predict the nonlinear systems. Second, we design a discrete-time dynamic event-triggered mechanism (ETM) and a state observer based on this ETM 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 obtained results. |
| Keywords: | discrete-time dynamic event-triggered mechanism disturbances linear matrix inequality (LMI) nonlinear systems |