An STL-Based Formulation of Resilience in Cyber-Physical Systems

Chen, Hongkai; Lin, Shan; Smolka, Scott A.; Paoletti, Nicola

doi:10.1007/978-3-031-15839-1_7

Cited by 6 publications

(14 citation statements)

References 24 publications

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“…We now give an overview of the formulation of STL resilience introduced in [6]. The intuition is that given an STL formula 𝜑, two properties characterize its resilience: recoverability and durability.…”

Section: Stl-based Resiliencementioning

confidence: 99%

“…Although various notions of resiliency have been proposed within a control setting [5,27], a general formal characterization has been lacking. Recently, Chen et al [6] used Signal Temporal Logic (STL) [16] to formally reason about resiliency in CPS. Given an STL property 𝜑 expressing a CPS requirement, the notion of resiliency introduced in [6] permits violations of 𝜑 as long as: 1) the CPS quickly recovers from the violation, and then 2) satisfies 𝜑 for an extended period of time.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Chen et al [6] used Signal Temporal Logic (STL) [16] to formally reason about resiliency in CPS. Given an STL property 𝜑 expressing a CPS requirement, the notion of resiliency introduced in [6] permits violations of 𝜑 as long as: 1) the CPS quickly recovers from the violation, and then 2) satisfies 𝜑 for an extended period of time. These two requirements are called recoverability and durability, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…The results of [6] naurally suggest the following problem of resilient STL control: find an optimal control strategy that maximizes the system's resilience in terms of recoverability and durability. These two objectives are often at odds with each other.…”

Section: Introductionmentioning

confidence: 99%

“…In this section, we provide background on the syntax and semantics of both STL and the STL-based Resiliency Specifications of [6].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An STL-based Approach to Resilient Control for Cyber-Physical Systems

Chen¹,

Smolka²,

Paoletti³

et al. 2022

Preprint

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We present ResilienC, a framework for resilient control of Cyber-Physical Systems subject to STL-based requirements. ResilienC utilizes a recently developed formalism for specifying CPS resiliency in terms of sets of (rec, dur ) real-valued pairs, where rec represents the system's capability to rapidly recover from a property violation (recoverability), and dur is reflective of its ability to avoid violations post-recovery (durability). We define the resilient STL control problem as one of multi-objective optimization, where the recoverability and durability of the desired STL specification are maximized. When neither objective is prioritized over the other, the solution to the problem is a set of Pareto-optimal system trajectories. We present a precise solution method to the resilient STL control problem using a mixed-integer linear programming encoding and an a posteriori 𝜖-constraint approach for efficiently retrieving the complete set of optimally resilient solutions. In ResilienC, at each time-step, the optimal control action selected from the set of Pareto-optimal solutions by a Decision Maker strategy realizes a form of Model Predictive Control. We demonstrate the practical utility of the ResilienC framework on two significant case studies: autonomous vehicle lane keeping and deadline-driven, multi-region package delivery.

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Section: Stl-based Resiliencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In this section, we provide background on the syntax and semantics of both STL and the STL-based Resiliency Specifications of [6].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An STL-based Approach to Resilient Control for Cyber-Physical Systems

Chen¹,

Smolka²,

Paoletti³

et al. 2022

Preprint

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Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guarantees

Cairoli,

Bortolussi,

Paoletti

2023

Lecture Notes in Computer Science

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Reusable Specification Patterns for Verification of Resilience in Autonomous Hybrid Systems

Adelt,

Mensing,

Herber

2024

Lecture Notes in Computer Science

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Autonomous hybrid systems are systems that combine discrete and continuous behavior with autonomous decision-making, e.g., using reinforcement learning. Such systems are increasingly used in safety-critical applications such as self-driving cars, autonomous robots or water supply systems. Thus, it is crucial to ensure their safety and resilience, i.e., that they function correctly even in the presence of dynamic changes and disruptions. In this paper, we present an approach to obtain formal resilience guarantees for autonomous hybrid systems using the interactive theorem prover KeYmaera X. Our key ideas are threefold: First, we derive a formalization of resilience that is tailored to autonomous hybrid systems. Second, we present reusable patterns for modeling stressors, detecting disruptions, and specifying resilience as a service level response in the differential dynamic logic (d$$\mathcal {L}$$ L ). Third, we combine these concepts with an existing approach for the safe integration of learning components using hybrid contracts, and extend it towards dynamic adaptations to stressors. By combining reusable patterns for stressors, observers, and adaptation contracts for learning components, we provide a systematic approach for the deductive verification of resilience of autonomous hybrid systems with reduced specification effort. We demonstrate the applicability of our approach with two case studies, an autonomous robot and an intelligent water distribution system.

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An STL-Based Formulation of Resilience in Cyber-Physical Systems

Cited by 6 publications

References 24 publications

An STL-based Approach to Resilient Control for Cyber-Physical Systems

An STL-based Approach to Resilient Control for Cyber-Physical Systems

Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guarantees

Reusable Specification Patterns for Verification of Resilience in Autonomous Hybrid Systems

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