It is well known that the optimal nonblocking supervisory control problem of timed discrete event systems is NP-hard, subject in particular to state space explosion that is exponential in the number of system components. In this thesis, we propose to manage complexity by organizing the system as a Timed State Tree Structure (TSTS). TSTS are an adaptation of STS to timed Supervisory Control Theory (SCT). Based on TSTS we present an efficient recursive symbolic algorithm that can perform nonblocking supervisory control design for systems of state size 1012 and higher.;Failure diagnosis is the process of detecting and identifying deviations of a system from its normal behavior using the information available through sensors. A method for fault diagnosis of the TSTS model is proposed. A state based diagnoser is constructed for each timed holon of TSTS. Fault diagnosis is accomplished using the state estimates provided by the timed holon diagnosers. The diagnosers may communicate among each other in order to update their state estimates. At any given time, only a subset of the diagnosers are operational, and as a result, instead of the entire model of the system, only the models of the timed holons associated with the operational diagnosers are used. It is shown that the computational complexity of constructing and storing the transition systems required for diagnosis in the proposed approach is polynomial in the number of system components, whereas in the original monolithic approach the computational complexity is exponential.
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