4. Model Detection

In this chapter, we first formulate the basic model detection task consisting of the identification of that particular model from a collection of linear time-invariant models, which best matches the current plant behaviour. Then, the concept of model detectability is introduced and characterized. Exact and approximate model detection problems are formulated and solvability conditions are derived. These problems target the synthesis of a bank of model detection filters which generate a structured set of residuals allowing the discrimination of models in the case of absence or presence of noise inputs, respectively. The discussion of specific performance requirements for model detection and the selection of thresholds to be used for decision-making conclude the chapter.