October 11, 2021: "Autonomous Systems Enabled by Control, Optimization, and Machine Learning"

• October 11, 2021
• 4:00 p.m.
• Zhenbo Wang, Ph.D., University of Tennessee Knoxville
• Faculty Host: Craig Woolsey

Abstract: Getting the vehicles to operate autonomously in highly uncertain, dynamic environments is still a solid challenge. Recent advances in control theory, optimization methods, and machine learning techniques provide unique opportunities for developing novel algorithms and architectures with the aim of achieving real-time, onboard applications for autonomous vehicle systems. The first part of this seminar will introduce the recent advancements in control, optimization, and machine learning and the roles they play in addressing several critical challenges in trajectory optimization and control of the vehicles. Potential ways to integrate these methodologies by combining their relative merits to develop fast, robust strategies for real-time implementations will be discussed as well. Then, the talk will show the applications of these methodologies for orbital transfers, atmospheric entry, pinpoint landing, and unmanned aerial vehicles. Finally, new challenges and potentially wider applications will be discussed.

Bio: Dr. Zhenbo Wang is currently an Assistant Professor in the Department of Mechanical, Aerospace, and Biomedical Engineering at the University of Tennessee Knoxville, where he also serves as the Director of the Autonomous Systems Laboratory. Dr. Wang received his Bachelor’s and Master’s degrees in Control Engineering in China. In 2018, he received his Ph.D. degree in Aerospace Engineering from the School of Aeronautics and Astronautics at Purdue University, West Lafayette, IN. His research interests are in the area of control and optimization, and specifically in using optimal control, convex optimization, and machine learning to improve the performance and autonomy of vehicle systems with different applications.