activity
20132026
most citedRun Time Assurance for Safety-Critical Systems: An Introduction to Safety Filtering Approaches for Complex Control Systems

76 citations · 213 across the 52 of their papers we have counts for

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Showing 2022Show all

7 papers · 1 filter

math.OC2022

Monotonicity and Contraction on Polyhedral Cones

Saber Jafarpour, Samuel Coogan

In this note, we study monotone dynamical systems with respect to polyhedral cones. Using the half-space representation and the vertex representation, we propose three equivalent c…

cs.LG2022★ 1 cited

Robust Training and Verification of Implicit Neural Networks: A Non-Euclidean Contractive Approach

Saber Jafarpour, Alexander Davydov, Matthew Abate +2

This paper proposes a theoretical and computational framework for training and robustness verification of implicit neural networks based upon non-Euclidean contraction theory. The…

eess.SY2022

Safe Schedule Verification for Urban Air Mobility Networks with Node Closures

Qinshuang Wei, Gustav Nilsson, Samuel Coogan

In Urban Air Mobility (UAM) networks, takeoff and landing sites, called vertiports, are likely to experience intermittent closures due to, e.g., adverse weather. To ensure safety,…

cs.RO2022★ 1 cited

Leveraging Heterogeneous Capabilities in Multi-Agent Systems for Environmental Conflict Resolution

Michael Enqi Cao, Jonas Warnke, Yunhai Han +3

In this paper, we introduce a high-level controller synthesis framework that enables teams of heterogeneous agents to assist each other in resolving environmental conflicts that ap…

cs.LG2022

Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks

Alexander Davydov, Saber Jafarpour, Matthew Abate +2

We use interval reachability analysis to obtain robustness guarantees for implicit neural networks (INNs). INNs are a class of implicit learning models that use implicit equations…

math.OC2022

Resilience of Input Metering in Dynamic Flow Networks

Saber Jafarpour, Samuel Coogan

In this paper, we study robustness of input metering policies in dynamic flow networks in the presence of transient disturbances and attacks. We consider a compartmental model for…

eess.SY2022★ 17 cited

Safe Learning for Uncertainty-Aware Planning via Interval MDP Abstraction

Jesse Jiang, Ye Zhao, Samuel Coogan

We study the problem of refining satisfiability bounds for partially-known stochastic systems against planning specifications defined using syntactically co-safe Linear Temporal Lo…