Robust Codiagnosability for Decentralized Diagnosis of Uncertain Discrete Event Systems

We consider a robust decentralized diagnosis problem for uncertain discrete event systems. We assume that, instead of the single exact model, a set of multiple possible models, each of which has its own event set and own normal behavior, is given for the task of diagnosis. The robust decentralized diagnosis problem requires us to synthesize a decentralized diagnoser such that, for all possible models, it detects the occurrence of any failure string within a finite number of steps. To characterize the existence of such a robust decentralized diagnoser, we introduce a notion of robust codiagnosability of the set of possible models. Then, we develop a method for verifying robust codiagnosability effectively.