Robust Residual Generation for Diagnosis Including a Reference Model for Residual Behavior
The main goal when synthesizing robust residual generators for
diagnosis and supervision is to attenuate influence from model
uncertainty on the residual while keeping fault detection performance.
Without considering structural constraints of the model, it is easy to
form unrealistic performance demands, and it is shown by examples how
this can lead to a design with unnecessary poor robustness properties.
Guided by the examples, a new design algorithm for robust residual
generators is developed. A key step is the design of a reference
model incorporating structural properties, and it is shown that this
leads to a consistent optimization problem avoiding unrealistic
demands. The available design freedom in the developed method is
explicit and intuitive, and the whole algorithm fits into the
framework of standard robust H_oo-filtering relying on established and
efficient methods. The designer of a diagnosis system is thus provided
with a method where it is easy to specify desired behavior without
violating structural requirements.
Erik Frisk and Lars Nielsen
Automatica,
2006
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