Minimal sensor activation and minimal communication in discrete-event systems

Sears, David; Rudie, Karen

doi:10.1007/s10626-015-0212-3

Cited by 26 publications

(14 citation statements)

References 38 publications

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“…6 also highlights different verification algorithms for different notions. For a comprehensive survey on the computational complexity of these verification algorithms, the reader is referred to Sears and Rudie (2015). Since all the notions in the diagram can be related from one to the other, an interesting question that arises is: How to choose the ''right'' approach to verify a certain property?…”

Section: Discussion and Summary Of Resultsmentioning

confidence: 99%

“…For instance, Ref. Huang et al (2008) presents a verification algorithm of complexity of O(|X | 6 |E|) for transition-based coobservability with two agents (|X | is the number of states and |E| is the number of events of the system); however, the extension of this algorithm to the case of n agents is not available in the literature (Sears & Rudie, 2015). However, by applying the transformation algorithm in Wang et al (2011) and the verification algorithm in Section 4, the verification of transition-based coobservability can be…”

Section: Discussion and Summary Of Resultsmentioning

confidence: 99%

“…This transformation from coobservability to codiagnosability makes it possible to leverage existing methodologies for solving (decentralized) diagnosis problems to solve (decentralized) control problems. However, to the best of our knowledge, the reverse transformation, from codiagnosability to coobservability, has remained an open problem, as mentioned in the recent survey (Sears & Rudie, 2015).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Codiagnosability and coobservability under dynamic observations: Transformation and verification

Yin

Lafortune

2015

Automatica

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Section: Discussion and Summary Of Resultsmentioning

confidence: 99%

Section: Discussion and Summary Of Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Codiagnosability and coobservability under dynamic observations: Transformation and verification

Yin

Lafortune

2015

Automatica

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“…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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“…Dynamic sensor activation has been studied extensively in the DES literature; see, e.g., [1]- [10] for a sample of this work and the recent survey paper [11] for an extensive bibliography. In [1], [2], the problem of dynamic sensor activation for the purpose of fault diagnosis was studied; the optimal synthesis problems considered therein were solved according to numerical cost criteria.…”

Section: Introductionmentioning

confidence: 99%

A general approach for solving dynamic sensor activation problems for a class of properties

Yin

Lafortune

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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We study the problem of dynamic sensor activation for centralized partially-observed discrete event systems. The sensors can be turned on/off online dynamically according to a sensor activation policy in order to satisfy some observation property. In this paper, we consider a general class of properties, called Information-State-based (or IS-based) properties, which include, but are not limited to, observability, K-diagnosability, predictability, and opacity. We define a new Most Permissive Observer (MPO) that generalizes previous versions of this structure. The MPO that we define embeds all sensor activation policies for an IS-based property. An optimal sensor activation policy can then be synthesized based on the MPO. Our results generalize the previous works on dynamic sensor activation for enforcing the properties of observability, K-diagnosability, and opacity. Moreover, our MPO is applicable to solving dynamic sensor activation problems for a wide class of user-defined properties that can be formulated as IS-based properties. As a special case, we show that the problem of minimal sensor activation for enforcing predictability, which has not been considered in the literature, is solvable by our new approach.

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Minimal sensor activation and minimal communication in discrete-event systems

Cited by 26 publications

References 38 publications

Codiagnosability and coobservability under dynamic observations: Transformation and verification

Codiagnosability and coobservability under dynamic observations: Transformation and verification

Estimation and Verification of Partially Observed Discrete‐Event Systems

A general approach for solving dynamic sensor activation problems for a class of properties

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