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Justin Lidard

PhD Student, Princeton University

I am a PhD student in Mechanical and Aerospace Engineering at Princeton, advised by Professor Naomi Leonard, where I will study multi-agent control theory, dynamical systems, machine learning, and their applications to robotics. I am fortunate to be supported by the Francis Robbins Upton Fellowship and the National Defense Science and Engineering Graduate (NDSEG) Fellowship. I have also recieved the NASA Space Technology Graduate Research Opportunities (NSTGRO; previously NSTRF) Fellowship.

In summer 2020, I did a research internship at Aurora Flight Sciences Cambridge. At Aurora, I worked on Gamebreaker, a DARPA project investigating quantitative modeling of balance in reinforcement learning games. Previously, I interned at the Johns Hopkins University Applied Physics Lab, where I worked on Tripwire, a system for undersea tracking and collision avoidance, and Stiletto, a low-cost prototype interceptor for hypersonic threats.

I did my undergrad at the University of Maryland, where I completed thesis research advised by Professor Derek Paley and funded by the ASPIRE Research Program for Undergraduates.

CV  /  Bio


Research

I'm interested in dynamical systems, control theory, optimization, perception, and machine learning. Much of my research is about modeling and interpreting the motion and stability of complex systems.

Feedback Control and Parameter Estimation for Lift Maximization of a Pitching Airfoil
Justin Lidard, Debdipta Goswami, David Snyder, Girguis Sedky, Anya Jones, Derek Paley
Submitted to AIAA JGCD, 2020
Lab website / Download citation

Data-driven method for augementing the lift of a rapidly pitching airfoil using a nonlinear control law and bifurcation theory. An extended Kalman filter is used to estimate the aerodyamic flow conditions and to predict the augmented lift output.

Output Feedback Control for Lift Maximization of a Pitching Airfoil
Justin Lidard, Debdipta Goswami, David Snyder, Girguis Sedky, Anya Jones, Derek Paley
AIAA SciTech, 2020
Lab website / See on AIAA / Download citation

Data-driven method for augementing the lift of a rapidly pitching airfoil using a nonlinear control law and bifurcation theory. An recursive Bayesian filter is implemented using an approximation of the Perron-Frobenius (PF) operator to summarize the nonlinear dynamics over a finite time horizon and estimate the flow state from lift measurements.


Service

umdaero Undergraduate Teaching Fellow, ENAE380

Peer Tutor, ENAE Academic Match Program

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