Autonomous systems can provide advantages such as access, expendability, and scaled force projection in adversarial environments. However, such environments are inherently complex in the sense they are uncertain and dynamically changing. This presentation provides a deep dive into learning for control and parameter estimation perspectives related to uncertainty, optimality, and data intermittency that provide foundations for assured autonomous operations. New results will be described for guaranteed deep learning methods that can be employed for real-time lifelong learning with no prior data.
Dr. Warren Dixon
His Ph.D. is in Electrical Engineering in 2000 from Clemson University, and after working at Oak Ridge National Laboratory as a Wigner Fellow and research staff member, he joined the University of Florida in 2004 and is now the Deanʼs Leadership Professor and Department Chair in the Department of Mechanical and Aerospace Engineering. His main research interest has been the development and application of Lyapunov-based control techniques for uncertain nonlinear systems. His work has been acknowledged by various early and mid-career awards and best paper awards. He is an ASME and IEEE Fellow for contributions to adaptive control of uncertain nonlinear systems.