Online supervision of intelligent vehicle using functional and behavioral models

Abstract

A supervision system oversees the operating state of an autonomous vehicle through the availability of the functions and services provided by the vehicle components. However, functional representations do not take into account the system dynamic behaviour and suffer from subjective definitions. For this reason, Bond Graph Models, through their behavioural, structural and causal properties are used to overcome the limitations of functional models. We obtain a new tool taking the form of a finite automaton, to provide fault identification and the reconfiguration conditions of a system. Each operating mode, corresponding to a vertex of the automaton, is associated with a set of services from a functional point of view and is defined accurately by a behavioral bond graph model. Furthermore, the service availability (associated to the Bond Graph elements) and the conditions for passage from one mode to another are analysed by fault detection and isolation algorithms generated on the basis of the structural and causal properties of the bond graph tool. The proposed approach is illustrated by a traction system of an intelligent and autonomous vehicle.