System-theoretic analysis and synthesis for fault-tolerant supervisory control of discrete event dynamic systems

Abstract

In this thesis, a system-theoretic approach is utilized to improve the reliability of a discrete event dynamic system (DEDS) from the fault-tolerance viewpoint. We discuss decentralized optimal fault-tolerant supervisory control issues on the basis of failure analysis and diagnosis from the angle of DEDSs. The development of theory includes : state identification, failure analysis, fault-tolerant supervisory control, failure diagnosis, layered optimal supervisory control, nonhomogeneous decentralized supervisory control, stability analysis, and robust supervisory control. First, we address the detectability problem for state identification and the observability problem for achievable legal languages. We also propose a systematic way for analyzing DEDSs to classify faults and failures quantitatively and to find tolerable fault event sequences embedded in the system. An automated failure diagnosis scheme with respect to the nominal normal operating event sequences and the supervisory control for tolerable fault event sequences are presented. Moreover, the supervisor failure diagnosis with respect to the tolerable fault event sequences is considered. Then a fault-tolerant supervisory control framework with partial observations is presented. Applications of the results to semiconductor manufacturing systems including the case study of a photolithographic process and a plasma etching process are described. Next, we consider the optimal supervisory control problem here. A complete mincut of the flow network induced from the finite state machine of a DEDS is introduced so that the soluting of the optimal supervisory control problem, which is transformed into an optimal state space partitioning problem, also includes the marked languages. In addition, the procedure for finding the achievable or nonachievable layered optimal legal sublanguages is suggested for a preferential option among the reachable states in the controlled plant. A layered optimal supervisory co...