This research project deals with the identification of discrete event system (DES) dynamics for synthesis of supervisory intelligent controllers. Such systems represent a complex plant's behavior by modeling it as a sequence of logical events, e.g., a computer disk control system. The supervisory controller is a DES controlling a hybrid system. Investigation of efficient methods for obtaining DES plant models are carried out in this project. Specific techniques used include the optimal designs of state space partitions defining the observed events using inductive inference procedures, with an emphasis on relatively low computational burden and reduced sample complexity.*** //
