Comitee

• Francesco BASILE, Professor, Università di Salerno (Italy), Reviewer
  
• Ernesto LOPEZ-MELLADO, Professor, Cinvestav Guadalajara (Mexico), Reviewer
  
• Hassane ALLA, Professor, Université Joseph Fourier, GIPSA-Lab (France), Examiner
• Bernard RIERA, Professor, Université de Reims-Champagne-Ardennes, CReSTIC (France), Examiner
  
• Jean-Jacques LESAGE, Professor, ENS Cachan (France), Advisor
  
• Gregory FARAUT, Assistant professor, ENS Cachan (France), Co-advisor
  

Keywords : Identification, Discrete Event Systems, Reactive Systems, Interpreted Petri Nets, Reverse Engineering

Résumé : This thesis proposes a method to identify compact and expressive models of closed-loop reactive Discrete Event Systems (DES), for reverse-engineering or certification. The identification is passive, and blackbox, accessible knowledge being limited to input/output signals. Interpreted Petri Nets (IPN) represent both the observable behaviour (direct input/output causalities) and the unobservable behaviour (internal state evolutions) of the system. This thesis aims at identifying IPN models from an observed sequence of I/O vectors. The proposed contributions extend previous results towards scalability, to deal with realistic systems who exhibit concurrency.

Firstly, the construction of the observable part of the IPN is improved by the addition of a filter limiting the effect of concurrency. It detects and removes spurious synchronizations caused by the controller. Then, a new approach is proposed to improve the discovery of the unobservable part. It is based on the use of projections and guarantees the reproduction of the observed behaviour, despite concurrency. An efficient heuristic is proposed to compute a model adapted to reverse-engineering, limiting the computational cost. Finally, a distributed approach is proposed to further reduce the computational cost, by automatically partitioning the system into subsystems. The efficiency of the cumulative effect of these contributions is validated on a system of realistic size.