Characterizing controllability and observability properties of temporal causal network modeling for discrete event systems

Control, fault monitoring, and diagnosis are critical tasks in managing discrete event systems such as real-world factory automation systems. We have applied a model-based technology based on temporal causal networks to the integrated modeling, diagnosis and reconfiguration of discrete event systems. Temporal causal networks use a propositional temporal logic with quantification over discrete time, in which the temporal sentences are constrained by the topology of the system structure that depicts the causal relations between system variables. This paper specifies for temporal causal networks some formal notions of control properties, such as observability and controllability, and the algorithmic approaches for computing these properties.