基于自适应未知输入观测器的多故障快速重构

doi:10.12305/j.issn.1001-506X.2022.07.34

Abstract

Abstract:

A scheme of multiple faults reconstruction using the fast adaptive unknown input observer (FAUIO) is proposed, which is used for a class of nonlinear systems where the faults enter the state equations via nonlinear functions. A proportional term is used to improve the rapidity of fault reconstruction. Specifically, actuator faults should be decomposed from the nonlinear function and an augmented descriptor system is preliminarily developed by constructing an augmented state composed of system states and sensor faults. Next, an H_∞ performance index is employed to prove the robust asymptotically stability via the state estimation error produced by the observer. Then, the problem of solving the designed FAUIO is transformed into a linear matrix inequalities (LMIs) constrained nonlinear optimization problem and solved by using the linear matrix inequality optimization technique, and the multiple faults reconstruction of actuator faults and sensor faults is further realized. Finally, the effectiveness of the developed FAUIO is validated via simulations of a single-link flexible joint robot.

Key words: multiple fault diagnosis, fault reconstruction, fast adaptive unknown input observer (FAUIO), nonlinear systems, linear matrix inequality (LMI)

CLC Number:

TP273

Sheng GAO, Guangfu MA, Yanning GUO. Fast reconstruction of multiple faults based on adaptive unknown input observer[J]. Systems Engineering and Electronics, 2022, 44(7): 2364-2373.

Figures/Tables 10

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References 28

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观测器的执行器故障重构结果	故障重构的准确性(RMSE)
	渐进故障	突变故障
	0.1(t－3)×r(t－3)－0.1(t－8)×r(t－8)	0.1(t－3)×r(t－3)－0.1(t－8)×r(t－3.001)－0.062 5(t－20)×r(t－20)+ 0.062 5(t－28)×r(t－20.001)
AO^[29]	0.072 3	0.132 6
AUIO^[23]	0.045 2	0.099 3
FAUIO	0.011 6	0.049 2

观测器的执行器故障重构结果	故障重构的快速性(t_s)
	渐进故障	突变故障
	0.1(t－3)×r(t－3)－0.1(t－8)×r(t－8)	0.1(t－3)×r(t－3)－0.1(t－8)×r(t－3.001)－0.062 5(t－20)×r(t－20)+ 0.062 5(t－28)×r(t－20.001)
AO^[29]	15.668	14.896
AUIO^[23]	8.987	7.526
FAUIO	2.123	2.323

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Fast reconstruction of multiple faults based on adaptive unknown input observer

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