Codiagnosability and coobservability under dynamic observations: Transformation and verification

Yin, Xiang; Lafortune, Stéphane

doi:10.1016/j.automatica.2015.08.023

Cited by 52 publications

(10 citation statements)

References 39 publications

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“…in [5] and in [24] where the observability with respect to a language [18] was generalized to the case of decentralized systems by introducing the notion of coobservability. In [32] and in [34] it was proven that coobservability and codiagnosability can be mapped from one to the other. Extending our approach to a decentralized framework and comparing our results with the ones above will be the subject of future investigation.…”

Section: Discussionmentioning

confidence: 99%

Observability and diagnosability of finite state systems: A unifying framework

Santis

Benedetto

2017

Automatica

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Abstract. In this paper, a general framework is proposed for the analysis and characterization of observability and diagnosability of finite state systems. Observability corresponds to the reconstruction of the system's discrete state, while diagnosability corresponds to the possibility of determining the past occurrence of some particular states, for example faulty states. A unifying framework is proposed where observability and diagnosability properties are defined with respect to a critical set, i.e. a set of discrete states representing a set of faults, or more generally a set of interest. These properties are characterized and the involved conditions provide an estimation of the delay required for the detection of a critical state, of the precision of the delay estimation and of the duration of a possible initial transient where the diagnosis is not possible or not required. Our framework makes it possible to precisely compare some of the observabllity and diagnosability notions existing in the literature with the ones introduced in our paper, and this comparison is presented.

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Section: Discussionmentioning

confidence: 99%

Observability and diagnosability of finite state systems: A unifying framework

Santis

Benedetto

2017

Automatica

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“…A general approach for dynamic sensor activation for property enforcement is proposed by [127]. In [106,117], it has been shown that (co)diagnosability and (co)observability can be mapped from one to the other in the general dynamic observation setting. The reader is referred to the recent survey [83] for more references on state estimation problem under dynamic observations.…”

Section: State Estimation Under General Observation Modelsmentioning

confidence: 99%

Estimation and Verification of Partially Observed Discrete‐Event Systems

Yin

2019

Wiley Encyclopedia of Electrical and Electronics Engineering

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State estimation is one of the most fundamental problems in the systems and control theory. In many real-world systems, it is not always possible to access the full information of the system due to measurement noises/uncertainties and the existence of adversaries. Therefore, how to estimate the state of the system is crucial when one wants to make decisions based on the limited and incomplete information.Given a system, one of the most important questions is to prove/disprove the correctness of the system with respect to some desired requirement (or specification). In particular, we would like to check the correctness of the system in a formal manner in the sense that the checking procedures are algorithmic and results have provable correctness guarantees. Such a formal satisfaction checking problem is referred to as the verification problem. Performing formal property verification is very important for many complicated but safety-critical infrastructures.This article considers state estimation and verification problems for an important class of man-made cyber-physical systems called Discrete-Event Systems (DES). Roughly speaking, DES are dynamic systems with discrete state-spaces and event-triggered dynamics. DES models are widely used in the study of complex automated systems where the behavior is inherently eventdriven, as well as in the study of discrete abstractions of continuous, hybrid, and/or cyber-physical systems. Over the past decades, the theory of DES has been successfully applied to many real-world problems, e.g., the control of

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“…The verification of codiagnosability of DES with dynamic observations is addressed in Wang et al (2011); Yin and Lafortune (2015). In Wang et al (2011), a verifier called C-VERIFIER, which is a nondeterministic automaton constructed using the notion of cluster automata, is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Since, as shown in Wang et al (2011), the number of states of the C-VERIFIER is O(|X| n+1 ), where n is the number of local agents, and it is a nondeterministic automaton, then the complexity for the computation of the C-VERIFIER is O(|X| 2(n+1) × |Σ|). The verifier presented in Yin and Lafortune (2015), called T-VERIFIER, is an extension of the standard verifier approach used for static diagnosis presented in Jiang et al (2001); Yoo and Lafortune (2002) to the case of dynamic observations. The T-VERIFIER is a deterministic automaton generated by using a set of rules in which event tuples are used to label the transitions, and the transition function is defined by computing four sets of local agents for each one of its transitions, according to the dynamic observation of the system events.…”

Section: Introductionmentioning

confidence: 99%

“…The T-VERIFIER is a deterministic automaton generated by using a set of rules in which event tuples are used to label the transitions, and the transition function is defined by computing four sets of local agents for each one of its transitions, according to the dynamic observation of the system events. As shown in Yin and Lafortune (2015), the number of states of the T-VERIFIER is in the worst-case equal to |X| n+1 , and the computational complexity of the T-VERIFIER is O(n × |X| n+1 × |Σ|).…”

Section: Introductionmentioning

confidence: 99%

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A New Algorithm for the Verification of Codiagnosability of Discrete Event Systems With Dynamic Observations

Silveira

Moreira

2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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In this work, a new algorithm for verifying the codiagnosticability of the language of a discrete event system (DES) with dynamic observations is proposed. The new algorithm is an extension of a recently proposed algorithm for the verification of the codiagnosability of DES with static observations. The computational complexity of the proposed algorithm is compared with the complexity of other algorithms presented in the literature.

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Codiagnosability and coobservability under dynamic observations: Transformation and verification

Cited by 52 publications

References 39 publications

Observability and diagnosability of finite state systems: A unifying framework

Observability and diagnosability of finite state systems: A unifying framework

Estimation and Verification of Partially Observed Discrete‐Event Systems

A New Algorithm for the Verification of Codiagnosability of Discrete Event Systems With Dynamic Observations

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