Robust Residual Generation for Diagnosis Including a Reference Model for Residual Behavior

Abstract

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. In this paper, a design algorithm for robust residual generators is developed with two key elements. One is the use of a reference model that represents desired performance, i.e. time or frequency-domain specifications of fault response in the residual. The other is an optimization criterion, based on robust H∞-filtering, used to synthesize the residual generator. Also, a methodology how to specify performance specifications to ensure good robustness properties of the optimal residual generator is developed.