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Autonomous Systems Enabled by Control, Optimization and Machine Learning

Zhenbo Wang, PhD
Assistant Professor
Mechanical, Aerospace and Biomedical Engineering
The University of Tennessee
Friday, October 30, 2020
3:30-4:30pm via ZOOM


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 the presentation will introduce the 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. Next, the presentation 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.


Dr. Zhenbo 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. Then he joined the University of Tennessee Knoxville as a tenure-track assistant professor in the Department of Mechanical, Aerospace, and Biomedical Engineering. 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 for different applications.