Abstract

Adaptive controllers employ online observations of system performance to determine control policies for driving a system toward a desired state. For example, the adaptive cruise control module in a car utilizes data from various sensors to steer the vehicle such that it maintains a safe following distance and stays within the speed limit. In this talk, I will introduce a set of learning algorithms to synthesize feedback control policies for dynamic systems. Specifically, I will discuss topics including event-triggered control, approximate dynamic programming, and the limits of learning-based controllers for real-time control.

Bio

Vignesh Narayanan (Member, IEEE) received the B.Tech. Electrical and Electronics Engineering degree from SASTRA University, Thanjavur, India, the M.Tech. degree with specialization in Control Systems from the National Institute of Technology Kurukshetra, Haryana, India, in 2012 and 2014, respectively, and the Ph.D. degree from the Missouri University of Science and Technology, Rolla, MO, USA, in 2017. He joined the Applied Mathematics Lab and Brain Dynamics and Control Research Group in the Dept. of Electrical and Systems Engineering at the Washington University in St. Louis, where he was a postdoctoral research associate. He is currently with the Dept. of Computer Science and Engineering and AI institute of University of South Carolina. He is also affiliated with CAN (Center for Autism and Neurodevelopmental disorders). His current research interests include learning and adaptation in dynamic population systems, complex dynamic networks, reinforcement learning, and computational neuroscience.

Location

In person: Swearingen Engineering Center in Room 2A31

Virtual MS Teams

Time

2:20-3:10pm