Hybrid Systems Diagnosability by Abstracting Faulty Continuous Dynamics

On-line model based reconfiguration is generally used to improve the ability of a system to tolerate faults. Recovery after fault occurrence relies on allowing the system to proceed with its mission from a new known nominal state. In this paper, we consider on-line reconfiguration from a novel point of view, having in mind to use reconfiguration actions to disambiguate the tracked estimated system state, i.e. to produce a more precise diagnosis. The choice of the best suited reconfiguration action(s) must hence be guided by the diagnosability properties of the system. However, diagnosability conditions known for continuous systems (CS) on one hand and for discrete event systems (DES) on the other hand cannot be applied directly because of the hybrid nature of the systems that we consider. Our work proposes a framework for analyzing the ability of a hybrid system that stands on recent results establishing the formal equivalence of diagnosability definitions for DES and CS. The approach relies on merging the fault signatures exhibited at the continuous level into the Mode Automaton that represents the discrete dynamics of the system, so that DES diagnosability analysis can be performed on the resulting Behavior Automaton and the corresponding diagnoser. When the state of the system is ambiguous, an analysis of the diagnoser allows us to point at reconfiguration actions that safely move the system into a mode reducing ambiguity.