Grado Department of Industrial and Systems Engineering
Ph.D., Aerospace Engineering, Georgia Institute of Technology, 2015
M.S., Mathematics, Virginia Tech, Virginia Tech, 2011
M.S., Aerospace Engineering, University of Napoli 'Federico II', 2006
B.S., Aerospace Engineering, University of Napoli 'Federico II', 2004
[R1] A L'Afflitto, (2018), Barrier Lyapunov functions and constrained model reference adaptive control, IEEE Control Systems Letters, 2(3): 441-446. [html]
[R2] RB Anderson, JA Marshall, and A L'Afflitto, 2020, Constrained robust model reference adaptive control of a tilt-rotor quadcopter pulling an unmodeled cart, IEEE Transactions on Aerospace and Electronic Systems. [html]
[R3] RB Anderson, JA Marshall, A L’Afflitto, and JM Dotterweich, 2020, Model reference adaptive control of switched dynamical systems with applications to aerial robotics, Journal of Intelligent & Robotic Systems, 100(3): 1265-1281. [html]
Constrained adaptive control: The fundamental problem addressed in this research concerns how to impose user-defined performance levels to a nonlinear plant affected by uncertainties and controlled by adaptive systems. In particular, in addition to enforcing asymptotic trajectory tracking, we address the problem of constraining both trajectories tracking error's transient and the control input at all times. This project has been supported by ARL, NSF, and ONR. See references [R1], [R2].
Robust adaptive control for hybrid systems: In this research, we created new adaptive controllers able to regulate nonlinear plants affected by uncertainties and external disturbances, and that are characterized by unknown, time-dependent switching functions. As applications of this work, we considered UAVs tasked with installing sensors on hard surfaces, and hence, experiencing unmodeled contact forces and instantaneous changes in dynamical models. This project has been supported by ARL, DARPA, and ONR. See reference [R3].
Tactical surveillance: This project concerns the problem of designing guidance and control algorithms for autonomous Class I multi-rotor unmanned aerial vehicles (UAVs) tasked with reaching some target area or surveilling a given area while minimizing the risk of detection from opponents, whose location is unknown. The UAVs employed in this research do not rely on any external source of information such as GPS signals, use passive sensors only, and perform all calculations aboard. This project has been supported by DARPA.