A Distributed Simplex Architecture for Multi-agent Systems

Mehmood, Usama; Stoller, Scott D.; Grosu, Radu; Roy, Shouvik; Damare, Amol; Smolka, Scott A.

doi:10.1007/978-3-030-91265-9_13

Cited by 8 publications

(2 citation statements)

References 28 publications

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“…Extending our method to hybrid systems is a direction for future work. Mehmood et al [14] propose a distributed Simplex architecture with BCs synthesized using control barrier functions (CBFs) and with switching conditions derived from the CBFs, which are BaCs satisfying additional constraints. A derivation of switching conditions based on Taylor approximation of CBFs is briefly described but does not consider the remainder error, admissible states, or restricted admissible states, and does not include a proof of correctness (which requires an analysis of the remainder error).…”

Section: Related Workmentioning

confidence: 99%

A Barrier Certificate-based Simplex Architecture with Application to Microgrids

Damare¹,

Roy²,

Smolka³

et al. 2022

Preprint

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We present SimpleMG, a new, provably correct design methodology for runtime assurance of microgrids (MGs) with neural controllers. Our approach is centered around the Neural Simplex Architecture, which in turn is based on Sha et al.'s Simplex Control Architecture. Reinforcement Learning is used to synthesize high-performance neural controllers for MGs. Barrier Certificates are used to establish SimpleMG's runtime-assurance guarantees. We present a novel method to derive the condition for switching from the unverified neural controller to the verified-safe baseline controller, and we prove that the method is correct. We conduct an extensive experimental evaluation of SimpleMG using RTDS, a high-fidelity, real-time simulation environment for power systems, on a realistic model of a microgrid comprising three distributed energy resources (battery, photovoltaic, and diesel generator). Our experiments confirm that SimpleMG can be used to develop high-performance neural controllers for complex microgrids while assuring runtime safety, even in the presence of adversarial input attacks on the neural controller. Our experiments also demonstrate the benefits of online retraining of the neural controller while the baseline controller is in control.

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Section: Related Workmentioning

confidence: 99%

A Barrier Certificate-based Simplex Architecture with Application to Microgrids

Damare¹,

Roy²,

Smolka³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Cooperation activities and collective behaviors are widespread phenomena in several environments, ranging from nature to human social relationships and artificial systems. Therefore, they have cross-cutting implications in different specific fields of knowledge, including, just to cite a few, biology ( Crall et al, 2019 ; Glen et al, 2019 ; Romanov et al, 2022 ), sociology ( Takano and Ichinose, 2018 ; Will et al, 2020 ), and robotics ( Dai et al, 2016 ; Rausch et al, 2020 ; Mehmood et al, 2021 ). Although different levels of abstraction are involved, information sharing mechanisms form the base for the evolution of biological, social, and engineering systems exhibiting the behaviors specified above.…”

Section: Introductionmentioning

confidence: 99%

On the Modeling and Verification of Collective and Cooperative Systems

Aldini

2022

Front. Robot. AI

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The formal description and verification of networks of cooperative and interacting agents is made difficult by the interplay of several different behavioral patterns, models of communication, scalability issues. In this paper, we will explore the functionalities and the expressiveness of a general-purpose process algebraic framework for the specification and model checking based analysis of collective and cooperative systems. The proposed syntactic and semantic schemes are general enough to be adapted with small modifications to heterogeneous application domains, like, e.g., crowdsourcing systems, trustworthy networks, and distributed ledger technologies.

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