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Eric Feron - Georgia Tech College of Engineering. Atlanta, GA, US

Eric Feron

Professor, Aerospace Engineering | Georgia Tech College of Engineering


Eric Feron uses fundamental concepts of control systems, optimization and computer science to address problems in aerospace engineering.



Eric Feron Publication Eric Feron Publication




Cyber Physical Systems Design and Analysis Nokia Distinguished Lecture: Eric Feron on Control Systems S




Eric Feron has been the Dutton-Ducoffe Professor of Aerospace Software Engineering at the Georgia Institute of Technology since 2005. Prior to that, he was on the faculty of MIT's department of Aeronautics and Astronautics from 1993 until 2005. He holds his BS, MS and PhD degrees from Ecole Polytechnique, France, Ecole Normale Suprieure, France and Stanford University, United States. Eric Feron's interests are to use fundamental concepts of control systems, optimization and computer science to address important problems in aerospace engineering, including: Aerobatic control of unmanned aerial vehicles, multi-agent operations, including air traffic control systems and aerospace software system certification. Eric Feron has published two books and several research papers; his former research students are distributed throughout academia, government and industry. He is an advisor to the Academy of Technologies, France. When he is not in his office, Eric Feron may be found sailing along the coast of Florida.

Areas of Expertise (4)

Flight Mechanics & Controls

Air Traffic Control Systems

Unmanned Aerial Vehicles

Aerospace Software Systems

Selected Accomplishments (5)

Advisor, French Academy of Technologies


ONR Young Investigator Award


NASA Certificate of Recognition


NSF Research Initiation Award


Charles Stark Draper Chair


Education (3)

Stanford University: Ph.D., Aerospace Engineering 1994

Ecole Polytechnique & Ecole Normale Superieure: M.S., Computer Science 1990

Ecole Polytechnique, Paris: B.S. 1989

Affiliations (2)

  • Applied Mathematics, Computer Science and Automation Laboratory at ENAC
  • Institut Supérieur de l’Aéronautique et de l’Espace (Supaéro)

Selected Media Appearances (4)

Wait for It: Why Boeing 737 Max Software Fix Is Taking So Long

Bloomberg  online


Software engineers need to ferret out ripple effects and unintended consequences, said Eric Feron, an aerospace software engineer at the Georgia Institute of Technology. “You have to look at the way the human is going to operate the plane. You have to consider the interactions with hardware, and other software,’’ he said. “We want to be sure, if we can be sure, that we have no negative interactions between software systems.’’

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Spotlight: Boeing's Safety Upgrade for 737 Max Jets No Easy Task: Aviation Experts

Xinhua  online


Eric Feron, professor of aerospace software engineering at the Georgia Institute of Technology, told Xinhua that there was a cross-compatibility problem with Boeing's willingness to make the 737 MAX 8 be "the same aircraft" as the previous 737 models, while improving its fuel consumption.

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Why Were Ethiopian Airlines’ Black Boxes Sent to Paris for Examination?

SLATE  online


The level of damage for the Ethiopian Airlines flight meant “the investigators need to be able to directly read into the memories of the black boxes, because the standard interfaces probably cannot be used,” says Eric Feron, professor of aerospace software engineering at Georgia Tech.

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Helicopters Teach Themselves to Do Aerial Maneuvers

Stanford Report  


"I think the range of maneuvers they can do is by far the largest" in the autonomous helicopter field, said Eric Feron, a Georgia Tech aeronautics and astronautics professor who worked on autonomous helicopters while at MIT. "But what's more impressive is the technology that underlies this work. In a way, the machine teaches itself how to do this by watching an expert pilot fly. This is amazing."...

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Selected Articles (4)

Monitor-Based Runtime Assurance for Temporal Logic Specifications

Electrical Engineering and Systems Science - Systems and Control

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

Electrical Engineering and Systems Science - Systems and Control

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

TM 2019, 13th USA/Europe Air Traffic Management Research and Development Seminar

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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Mixed Integer Programming for Aircraft Conflict Resolution

American Institute of Aeronautics and Astronautics

Eric Feron, Antonio Bicchi and Lucia Pallottino


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