Auxiliary model-based interval-varying maximum likelihood estimation for nonlinear systems with missing data

The 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.

Automated summary

This article focuses on the identification problem of nonlinear systems with missing data. To overcome the unavailability of system 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. A maximum likelihood-based interval-varying recursive least-squares method is investigated based on the maximum likelihood principle and the least-squares method. The validity of the proposed maximum likelihood method is tested through numerical simulation and a practical continuous stirred tank reactor (CSTR) process.