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Fault‐Tolerant Control Based on Augmented State Estimator and PDF
Author(s) -
Li Tao,
Dai Zhuxiang,
Chen Long,
Cheng Yi
Publication year - 2020
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1941
Subject(s) - estimator , control theory (sociology) , fault tolerance , fault (geology) , state (computer science) , artificial neural network , computer science , engineering , mathematics , control (management) , algorithm , statistics , reliability engineering , artificial intelligence , seismology , geology
A new fault‐tolerant control based on augmented state estimator and probability density function (PDF) is proposed for a stochastic distribution system (SDS) with time‐delay and additive fault. First, a system model based on a PDF with the additive fault is constructed by using square‐root rational B‐spline neural networks. Second, an augmented system is obtained by converting the additive fault as an auxiliary state variable. In this framework, a robust augmented state estimator is designed to estimate the original states and the additive fault simultaneously. Then, based on the obtained estimation of fault, a delay‐dependent fault‐tolerant control is designed to compensate the fault. Finally, the numerical simulations show the effectiveness of the proposed method.