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

被引:7
作者
Huong, D. C. [1 ]
Nguyen, T. N. [1 ]
Le, H. T. [1 ]
Trinh, H. [2 ]
机构
[1] Quy Nhon Univ, Dept Math & Stat, Qui Nhon, Binh Dinh, Vietnam
[2] Deakin Univ, Sch Engn, Geelong, Vic, Australia
关键词
discrete-time dynamic event-triggered mechanism; disturbances; linear matrix inequality (LMI); nonlinear systems; SLIDING MODE CONTROL; NEURAL-NETWORKS; TIME; OBSERVERS; STABILITY; DESIGN;
D O I
10.1002/asjc.3054
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:4058 / 4069
页数:12
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