Titlebook: Estimation and Inference in Discrete Event Systems; A Model-Based Approa Christoforos N. Hadjicostis Book 2020 Springer Nature Switzerland

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State Estimation,nt system (DES). These tasks include fault diagnosis and event inference, which are directly related to state estimation, as well as supervisory control and optimization/scheduling problems, which are indirectly related to state estimation. This chapter discusses state estimation in DES that are mod

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Verification of State Isolation Properties,tial) based on a streaming sequence of observations that is generated by the activity in the underlying finite automaton. In particular, given a subset of states ., we say that the (current, delayed, or initial) state of the system has been “isolated within .” if, following a sequence of observation

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Detectability,rministic finite automaton with outputs, will almost always allow an external observer to pinpoint the (current) state of the system. More specifically, the most basic formulation, called strong detectability, requires that, regardless of the actual activity that may occur in the system, the externa

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Diagnosability,earch community. The typical set up in this line of work is to assume that the system is described by a (deterministic or nondeterministic) finite automaton that is known precisely; however, the initial state of the system may only be partially known and the activity that is taking place may only be

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Opacity,stems) has prompted increased attention on various aspects of privacy and security. This chapter considers systems that can be modeled as deterministic finite automata with outputs (and possibly silent transitions) and labeled nondeterministic finite automata, and discusses notions of opacity, which

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Decentralized State Estimation,ight be observed by multiple observation sites with distinct observation capabilities. We focus on the case of a single (monolithic) system that is modeled as a labeled nondeterministic finite automaton and assume that each observation site has its own natural projection observation mapping (i.e., e