On approximate predictability of metric systems

Fiore, Gabriella; Santis, Elena De; Pola, Giordano; Benedetto, Maria Domenica Di

doi:10.1016/j.ifacol.2018.08.029

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…For this reason, the notion of approximate (bi)simulation has become a keystone in a broad range of applications, namely compositional reasoning [23], controller synthesis [24]- [26] and fault detection [27], [28]. Several modifications to the original notion of approximate (bi)simulation have given rise to different variations, such as ε-(bi)simulation [29], strong η-approximate (bi)simulation [27], alternating εapproximate (bi)simulation [30], and ( , δ)-approximate (bi)simulation [31], [32], to name but a few.…”

Section: Introductionmentioning

confidence: 99%

A Notion of System Comparison

Pirastehzad¹,

Schaft²,

Besselink³

2023

Preprint

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We introduce (γ,δ)-similarity, a notion of system comparison that measures to what extent two dynamical systems behave similarly in an input-output sense. This behavioral similarity is characterized by measuring the sensitivity of the difference between the two output trajectories in terms of the external inputs to the two potentially nondeterministic systems. As such, (γ,δ)-similarity is a notion that characterizes approximation of input-output behavior, whereas existing notions of simulation target equivalence. Next, as this approximation is specified in terms of the L 2 signal norm, (γ,δ)-similarity allows for integration with existing methods for analysis and synthesis of control systems, in particular, robust control techniques. We characterize the notion of (γ,δ)-similarity as a linear matrix inequality feasibility problem and derive its interpretations in terms of transfer matrices. Our study on the compositional properties of (γ,δ)similarity shows that the notion is preserved through series and feedback interconnections. This highlights its applicability in compositional reasoning, namely abstraction and modular synthesis of large-scale interconnected dynamical systems. We further illustrate our results in an electrical network example.

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Section: Introductionmentioning

confidence: 99%

A Notion of System Comparison

Pirastehzad¹,

Schaft²,

Besselink³

2023

Preprint

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“…Finally, approximate notions of two related properties called diagnosability and predictability are investigated recently in [PDSDB18,FDSPDB18]. Their setting is very similar to us as we both consider a measurement uncertainty threshold.…”

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confidence: 99%

Towards approximate opacity of cyber-physical system

Yin

2019

Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems

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Opacity is an important information-flow security property in the analysis of cyber-physical systems. It captures the plausible deniability of the system's secret behavior in the presence of an intruder that may access the information flow. Existing works on opacity only consider non-metric systems by assuming that the intruder can always distinguish two different outputs precisely. In this paper, we extend the concept of opacity to systems whose output sets are equipped with metrics. Such systems are widely used in the modeling of many real-world systems whose measurements are physical signals. A new concept called approximate opacity is proposed in order to quantitatively evaluate the security guarantee level with respect to the measurement precision of the intruder. Then we propose a new simulation-type relation, called approximate opacity preserving simulation relation, which characterizes how close two systems are in terms of the satisfaction of approximate opacity. This allows us to verify approximate opacity for large-scale, or even infinite systems, using their abstractions. We also discuss how to construct approximate opacity preserving symbolic models for a class of discrete-time control systems. Our results extend the definitions and analysis techniques for opacity from non-metric systems to metric systems.

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