scholarly journals Design of nonlinear parity approach to fault detection and identification based on Takagi-Sugeno fuzzy model and unknown input observer in nonlinear systems

2020 ◽  
Vol 14 (3) ◽  
pp. 1-11
Author(s):  
Hamed Tolouei ◽  
◽  
Mahdi Aliyari Shoorehdeli ◽  
Keyword(s):  
Nonlinear Systems ◽  
Fault Detection ◽  
Fuzzy Model ◽  
Unknown Input ◽  
Unknown Input Observer ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Takagi Sugeno

scholarly journals Robust nonlinear observer design for actuator fault detection in diesel engines

2013 ◽  
Vol 23 (3) ◽  
pp. 557-569 ◽  
Author(s):  
Boulaid Boulkroune ◽  
Issam Djemili ◽  
Abdel Aitouche ◽  
Vincent Cocquempot
Keyword(s):  
Nonlinear Systems ◽  
Fault Detection ◽  
Estimation Error ◽  
Nonlinear Observer ◽  
Disturbance Attenuation ◽  
Mean Value Theorem ◽  
Unknown Input ◽  
Actuator Fault ◽  
Unknown Input Observer ◽  
Varying Coefficients

Abstract This paper is concerned with actuator fault detection in nonlinear systems in the presence of disturbances. A nonlinear unknown input observer is designed and the output estimation error is used as a residual for fault detection. To deal with the problem of high Lipschitz constants, a modified mean-value theorem is used to express the nonlinear error dynamics as a convex combination of known matrices with time-varying coefficients. Moreover, the disturbance attenuation is performed using a modified H∞ criterion. A sufficient condition for the existence of an unknown input observer is obtained using a linear matrix inequality formula, and the observer gains are obtained by solving the corresponding set of inequalities. The advantages of the proposed method are that no a priori assumption on the unknown input is required and that it can be applied to a large class of nonlinear systems. Performances of the proposed approach are shown through the application to a diesel engine model.

Fault Detection and Isolation of Nonlinear Systems: An Unknown Input Observer Approach With Sum-of-Squares Techniques

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Jun Xu ◽  
Kai-Yew Lum ◽  
Lihua Xie ◽  
Ai-Poh Loh
Keyword(s):  
Nonlinear Systems ◽  
Fault Detection ◽  
Design Method ◽  
Design Procedure ◽  
Sum Of Squares ◽  
Fault Detection And Isolation ◽  
Unknown Input ◽  
Unknown Input Observer ◽  
Lie Geometry ◽  
Rank Constraint

This paper presents a novel nonlinear unknown input observer (UIO) design method for fault detection and isolation (FDI) of a class of nonlinear affine systems. By using sum-of-squares (SOS) theory and Lie geometry as the main tools, we demonstrate how to relax the rank constraint in the traditional UIO approach and simplify the design procedure, especially for the polynomial nonlinear systems. Meanwhile, we show that the detection and isolation thresholds based on the L2 gains can be easily obtained via optimization formulated in terms of SOS. Simulation examples are given to illustrate the design procedure and the advantages.

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.

scholarly journals A Neuro-Augmented Observer for Robust Fault Detection in Nonlinear Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Huajun Gong ◽  
Ziyang Zhen
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Fault Detection ◽  
Rbf Neural Network ◽  
Estimation Error ◽  
Accurate Estimation ◽  
Detection Scheme ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Approach Zero

A new fault detection method using neural-networks-augmented state observer for nonlinear systems is presented in this paper. The novelty of the approach is that instead of approximating the entire nonlinear system with neural network, we only approximate the unmodeled part that is left over after linearization, in which a radial basis function (RBF) neural network is adopted. Compared with conventional linear observer, the proposed observer structure provides more accurate estimation of the system state. The state estimation error is proved to asymptotically approach zero by the Lyapunov method. An aircraft system demonstrates the efficiency of the proposed fault detection scheme, simulation results of which show that the proposed RBF neural network-based observer scheme is effective and has a potential application in fault detection and identification (FDI) for nonlinear systems.

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