TITLE:
Backstepping-Based Distributed Abnormality Detection for Nolinear Parabolic Distributed Prameter Systems
AUTHORS:
Lei Chen
KEYWORDS:
Abnormality Detection, Backstepping, Nonlinear Parabolic Systems, Distributed Parameter Systems, Lyapunov Function
JOURNAL NAME:
Engineering,
Vol.14 No.7,
July
28,
2022
ABSTRACT: In this paper, we proposed a model-based abnormality detection scheme for a class of nonlinear parabolic distributed parameter systems (DPSs). The proposed methodology consists of the design of an observer and an abnormality detection filter (ADF) based on the backstepping technique and a limited number of in-domain measurements plus one boundary measurement. By taking the difference between the measured and estimated outputs from observer, a residual signal is generated for fault detection. For the detection purpose, the residual is evaluated in a lumped manner and we propose an explicit expression for the time-varying threshold. The convergence properties of the PDE observer and the residual are analyzed by Lyapunov stability theory. Eventually, the proposed abnormality detection scheme is demonstrated on a nonlinear DPS.