Abnormal source identification for parabolic distributed parameter systems

TL;DR

This paper introduces a novel inverse spatio-temporal model for identifying abnormal sources in distributed parameter systems, using only system output and ensuring convergence through theoretical analysis.

Contribution

It is the first to develop an inverse S-T model for abnormal source identification in DPSs, combining an adaptive observer and estimation algorithm without requiring state measurements.

Findings

Successful identification of abnormal sources in a heat transfer rod

The proposed method guarantees convergence of estimation errors

Only system output is needed for source identification

Abstract

Identification of abnormal source hidden in distributed parameter systems (DPSs) belongs to the category of inverse source problems. It is important in industrial applications but seldom studied. In this paper, we make the first attempt to investigate the abnormal spatio-temporal (S-T) source identification for a class of DPSs. An inverse S-T model for abnormal source identification is developed for the first time. It consists of an adaptive state observer for source identification and an adaptive source estimation algorithm. One major advantage of the proposed inverse S-T model is that only the system output is utilized, without any state measurement. Theoretic analysis is conducted to guarantee the convergence of the estimation error. Finally, the performance of the proposed method is evaluated on a heat transfer rod with an abnormal S-T source.