Affiliation:
1. Taizhou Electric Power Conversion and Control Engineering Technology Research Center Taizhou University Taizhou China
2. School of Internet of Things Engineering Wuxi University Wuxi China
Abstract
AbstractThe identification problem of nonlinear system with missing data is focused in this article. In order to overcome the system unavailable outputs, an auxiliary model‐based interval‐varying recursive identification method is derived by changing the sampling interval and substituting the missing output with the output of an auxiliary model. Based on the maximum likelihood principle and the least‐squares method, a maximum likelihood‐based interval‐varying recursive least‐squares method is investigated. The validity of the proposed maximum likelihood method is tested by a numerical simulation example and a practical continuous stirred tank reactor (CSTR) process.
Funder
National Natural Science Foundation of China
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Mechanical Engineering,Aerospace Engineering,Biomedical Engineering,General Chemical Engineering,Control and Systems Engineering