Eric Feron

Advisor, French Academy of Technologies

2001

ONR Young Investigator Award

1999

NASA Certificate of Recognition

1998

Stanford University

Ph.D.

Aerospace Engineering

1994

Ecole Polytechnique & Ecole Normale Superieure

M.S.

Computer Science

1990

Ecole Polytechnique, Paris

B.S.

1989

Monitor-Based Runtime Assurance for Temporal Logic Specifications

Matthew Abate, Eric Feron, Samuel Coogan

2019

This paper introduces the safety controller architecture as a runtime assurance mechanism for system specifications expressed as safety properties in Linear Temporal Logic (LTL). The safety controller has three fundamental components: a performance controller, a backup controller, and an assurance mechanism. The assurance mechanism uses a monitor, constructed as a finite state machine (FSM), to analyze a suggested performance control input and search for system trajectories that are bad prefixes of the system specification. A fault flag from the assurance mechanism denotes a potentially dangerous future system state and triggers a sequence of inputs that is guaranteed to keep the system safe for all time. A case study is presented which details the construction and implementation of a safety controller on a non-deterministic cyber-physical system.

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Lyapunov Differential Equation Hierarchy and Polynomial Lyapunov Functions for Switched Linear Systems

Matthew Abate, Samuel Coogan, Eric Feron

2019

In this work, we address the problem of searching for homogeneous polynomial Lyapunov functions for stable switched linear systems. Specifically, we show an equivalence between polynomial Lyapunov functions for switched linear systems and quadratic Lyapunov functions for a related hierarchy of Lyapunov differential equations. This creates an intuitive procedure for checking the stability of properties of switched linear systems, and an algorithm is presented for generating high-order homogeneous polynomial Lyapunov functions in this manner.

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Predicting and Analyzing US Air Traffic Delays using Passenger-centric Data-sources

Philippe Monmousseau, Daniel Delahaye, Aude Marzuoli, Eric Féron

2019

This paper aims at presenting a novel way of predicting and analyzing air traffic delays using publicly available data from social media with a focus on Twitter data. Three different machine learning regressors have been trained on this 2017 passenger-centric dataset and tested for the prediction up to five hours ahead of air traffic delays and cancellations for the first two months of 2018. Comparing and analyzing different accuracy measures of their prediction performances show that this dataset contains useful information about the current state and short-term future state of the air traffic system. The resulting methods yield higher prediction accuracy than traditional state-of-the-art and off-the-shelf time-series forecasting techniques performed on flight-centric data. More over a post-training feature importance analysis conducted on the Random Forest regressor allowed a simplification and a refining of the model, leading to a faster training time and more accurate predictions. This paper is a first step in predicting and analyzing air traffic delays leveraging a real-time publicly available passenger-centered data source. The results of this study suggest a method to use passenger-centric data-sources both as an estimator of the current state of air traffic delays as well as an estimator of the short-term state of air traffic delays in the United States in real-time.

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