Robust model matching for geometric fault detection filters

Geometric fault detection and isolation filters are known for having excellent fault isolation properties. However, they are generally assumed to be sensitive to model uncertainty and noise. This paper proposes a robust model matching method to incorporate model uncertainty into the design of geometric fault detection filters. Several existing methods for robust filter synthesis are described to solve the robust model matching problem. It is then shown that the robust model matching problem has an interesting self-optimality property for multiplicative input uncertainty models. Finally, a simple example is presented to study the effect of parametric uncertainty and unmodeled dynamics on the performance of a geometric filter.