Fault residual generation via nonlinear analytical redundancy

Fault detection is critical in many applications, and analytical redundancy (AR) has been the key underlying tool for many approaches to fault detection. However, the conventional AR approach is formally limited to linear systems. In this brief, we exploit the structure of nonlinear geometric contro...

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Bibliographic Details
Published in:IEEE transactions on control systems technology 2005-05, Vol.13 (3), p.452-458
Main Authors: Leuschen, M.L., Walker, I.D., Cavallaro, J.R.
Format: Article
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Control systems
Control theory
Control theory. Systems
Exact sciences and technology
Fault detection
Linear systems
Mathematical analysis
Nonlinear dynamical systems
Nonlinear systems
Nonlinearity
Observability
Power engineering and energy
Redundancy
residuals
robotics
Robots
System testing
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Summary:Fault detection is critical in many applications, and analytical redundancy (AR) has been the key underlying tool for many approaches to fault detection. However, the conventional AR approach is formally limited to linear systems. In this brief, we exploit the structure of nonlinear geometric control theory to derive a new nonlinear analytical redundancy (NLAR) framework. The NLAR technique is applicable to affine systems and is seen to be a natural extension of linear AR. The NLAR structure introduced in this brief is tailored toward practical applications. Via an example of robot fault detection, we show the considerable improvement in performance generated by the approach compared with the traditional linear AR approach.
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2004.839577