Riccardo Bevilacqua
- Title
- Interim Associate Dean and Professor
- bevilacr@erau.edu Email
- Department
- Aerospace Engineering Department
- College
- College of Engineering
External Links
The views expressed on faculty and external web pages are those of the author and do not necessarily represent those of Embry-Riddle Aeronautical University.
Biography
Dr. Riccardo Bevilacqua is a Professor of Aerospace Engineering at Embry-Riddle Aeronautical University, Daytona Beach Campus. He holds a M.Sc. in Aerospace Engineering (2002), and a Ph.D. in Applied Mathematics (2007), both from the University of Rome, "Sapienza", Italy. Dr. Bevilacqua is the recipient of two Young Investigator Awards, from the Air Force Office of Scientific Research (2012) and the Office of Naval Research (2013), of the 2014 Dave Ward Memorial Lecture Award from the Aerospace Controls and Guidance Systems Committee, and of four Air Force Summer Fellowships (2012, 2015 and 2021 at AFRL Space Vehicle Directorate, 2019 and 2020 at AFRL Munitions Directorate). His research interests focus on spacecraft formation flight, space robotics and warheads/spacecraft fragment fly-out predictions. He has authored and co-authored more than 100 journal and conference publications on the topics. He is an AIAA Associate Fellow, IAA Full Member, and AAS Fellow. He is the founder and chair of the IAA conference on Space Situational Awareness.
Education
- Ph.D. - Doctor of Philosophy in Mathematical Methods and Models, Universita degli Studi di Roma La Sapienza
- M. - Master in Aerospace Engineering, Universita degli Studi di Roma La Sapienza
- B. - Bachelor in Aerospace Engineering, Universita degli Studi di Roma La Sapienza
Currently Teaching
- AE 800: Dissertation
- AE 700: Thesis
Research Projects
- Novel Space Science Test via Adaptive Control and Integral Concurrent Learning Leveraging On-Orbit CubeSat Structural Identification
- GNC Efforts in Support of the University of Floridas Research for the NASA Instrument Incubator
- CubeSats Hosting Flexible Appendages for On-Orbit Testing of Advanced Control Algorithms
- A Machine Learning Based Transfer to Predict Warhead In-Flight Behavior from Static Arena Test Data
Publications
- Six-degree-of-freedom Optimal Feedback Control of Pinpoint Landing using Deep Neural Networks Pure Portfolio (2023)
- Stereoscopic-Based Mass Properties Estimation for Warhead Fragments Pure Portfolio (2023)
- Modeling and Estimation of a Continuous Flexible Structure using the Theory of Functional Connections Math Department Colloquium Series (2023)
- Modeling and Estimation of a Continuous Flexible Structure using the Theory of Functional Connections Pure Portfolio (2023)
- Using Machine Learning to Predict Hypervelocity Fragment Propagation of Space Debris Collisions Pure Portfolio (2023)
- State Space Modeling and Estimation of Flexible Structure Using the Theory of Functional Connections Pure Portfolio (2023)
- Stability of Deep Neural Networks for Feedback-Optimal Pinpoint Landings Pure Portfolio (2023)
- A Machine Learning Based Transfer to Predict Warhead In-Flight Behavior from Static Arena Test Data Faculty Research Projects (2023)
- Novel Space Science Test via Adaptive Control and Integral Concurrent Learning Leveraging On-Orbit CubeSat Structural Identification Faculty Research Projects (2023)
- GNC Efforts in Support of the University of Florida's Research for the NASA Instrument Incubator Faculty Research Projects (2023)
- CubeSats Hosting Flexible Appendages for On-Orbit Testing of Advanced Control Algorithms Faculty Research Projects (2023)
- Novel Space Science Test via Adaptive Control and Integral Concurrent Learning Leveraging On-Orbit CubeSat Structural Identification Pure Portfolio (2023)
- CubeSats Hosting Flexible Appendages for On-Orbit Testing of Advanced Control Algorithms Pure Portfolio (2023)
- A Machine Learning Based Transfer to Predict Warhead In-Flight Behavior from Static Arena Test Data Pure Portfolio (2023)
- GNC Efforts in Support of the University of Florida's Research for the NASA Instrument Incubator Pure Portfolio (2023)
- Predicting Dynamic Fragmentation Characteristics from High-Impact Energy Events Utilizing Terrestrial Static Arena Test Data and Machine Learning Pure Portfolio (2023)
- Experimental validation of inertia parameters and attitude estimation of uncooperative space targets using solid state LIDAR Pure Portfolio (2023)
- Dual Quaternion Relative Dynamics for Gravity Recovery Missions Pure Portfolio (2023)
- Solar Sailing Adaptive Control Using Integral Concurrent Learning for Solar Flux Estimation Pure Portfolio (2023)
- Feedback Control Methods on Short-Period Orbits Of the Earth-Moon Equilateral Libration Points Pure Portfolio (2023)