Robust Model Matching for Geometric Fault Detection Filters: A Commercial Aircraft Example ?

Geometric fault detection and isolation filters are known for having excellent fault isolation, fault reconstruction and sensitivity properties under small modeling uncertainty and noise. However they are assumed to be sensitive to model uncertainty and noise. This paper proposes a method to incorporate model uncertainty into the design. First, a geometric filter is designed on the nominal plant. Next a robust model matching problem is solved to design a filter that robustly matches the performance of the geometric filter over the set of uncertain plants. 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 sets. Finally, an aircraft dynamics example is presented to detect and isolate aileron actuator faults to asses the performance of the geometric filter.