Robust Unknown Input Observer for Nonlinear Systems and Its Application to Fault Detection and Isolation

2008 ◽  
Vol 130 (4) ◽  
Author(s):  
S. Mondal ◽  
G. Chakraborty ◽  
K. Bhattacharyya
Keyword(s):  
Fault Diagnosis ◽  
Nonlinear System ◽  
Fault Detection ◽  
Nonlinear Function ◽  
Fault Detection And Isolation ◽  
Linear Matrix ◽  
Unknown Input ◽  
Matrix Inequality ◽  
Unknown Input Observer ◽  
Linear Matrix Inequality Approach

A robust unknown input observer for a nonlinear system whose nonlinear function satisfies the Lipschitz condition is designed based on linear matrix inequality approach. Both noise and uncertainties are taken into account in deriving the observer. A component fault detection and isolation scheme based on these observers is proposed. The effectiveness of the observer and the fault diagnosis scheme is shown by applying them for component fault diagnosis of an electrohydraulic actuator.

An unknown input observer-based decentralized fault detection and isolation for a class of large-scale interconnected nonlinear systems

2017 ◽  
Vol 40 (8) ◽  
pp. 2599-2606 ◽  
Author(s):  
Amir Abbasi ◽  
Javad Poshtan
Keyword(s):  
Nonlinear Systems ◽  
Fault Detection ◽  
Large Scale ◽  
Sufficient Conditions ◽  
Fault Detection And Isolation ◽  
Linear Matrix ◽  
Unknown Input ◽  
Unknown Input Observer ◽  
High Convergence Rate ◽  
Interconnected Nonlinear Systems

In this paper, using a bank of decentralized nonlinear unknown input observers, a novel scheme for actuator fault detection and isolation of a class of large-scale interconnected nonlinear systems is presented. For each of the interconnected subsystems, a local nonlinear unknown input observer is designed without the need to communicate with other agents. Sufficient conditions for the observer existence are derived based on the Lyapunov stability theory. To facilitate the observer design, the achieved conditions are formulated in terms of a set of linear matrix inequalities and optimal gain matrices are obtained. By using both system output and its difference with the estimated output in observer equation, each local observer shows a high convergence rate. Simulation of an automated highway system is used to demonstrate the effectiveness of the proposed methodology.

Fault Detection and Isolation in Power Systems Using Unknown Input Observer

2019 ◽  
pp. 38-58 ◽  
Author(s):  
Hassan Haes Alhelou
Keyword(s):  
Fault Detection ◽  
Power Systems ◽  
Frequency Control ◽  
Load Frequency Control ◽  
Fault Detection And Isolation ◽  
Unknown Input ◽  
Unknown Input Observer ◽  
Control Loops ◽  
Interconnected Power Systems ◽  
Unknown Disturbance

Unknown input observers (UIO) find application in many cases for successful fault detection and isolation (FDI). In this chapter, a scenario where the unknown input observer is applied to load frequency control loops in interconnected power systems is analyzed. A UIO was chosen because load demand is not always constant and it can be considered to introduce an unknown disturbance to the system. Mathematical formulations on how to detect and isolate sensor faults are presented which are then implemented in MATLAB Simulink for simulations. Based on this historical survey on the application of UIO, a thesis on UIO application in FDI in distributed generation is done.

Linear-Matrix-Inequality-Based Fault Diagnosis for Gas Turbofan Engine Using Eigenstructure Assignment Principle

2013 ◽  
Vol 302 ◽  
pp. 759-764 ◽  
Author(s):  
Yue Liu ◽  
Dao Liang Tan ◽  
Bin Wang ◽  
Xi Wang
Keyword(s):  
Fault Detection ◽  
Linear Matrix Inequality ◽  
Aircraft Engine ◽  
Fault Detection And Isolation ◽  
Linear Matrix ◽  
Eigenstructure Assignment ◽  
Matrix Inequality ◽  
Turbofan Engine ◽  
Suggested Approach ◽  
Assignment Method

This paper proposes an eigenstructure assignment method for engine control system diagnosis based on disturbance decoupling, since noisy disturbance has an adverse impact on the performance of aircraft engine fault detection and isolation (FDI). In practice, it is often difficult to solve the eigenstructure assignment method, and the result is far from being satisfactory. In view of this, the paper makes an attempt to deal with the issue by linear matrix inequality (LMI). The advantages of the presented method are as follows: first, it can reduce the effect of exogenous disturbance on fault detection; In the meantime, it will not impair sensitivity to system faults. Experimental results show that the suggested approach performs well on the simulation of an advanced turbofan engine.

Sign in / Sign up

Export Citation Format

Share Document