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Abstract: This talk will look at some recent results in optimal control theory for sampled data
control and networked systems. Sampled-data control systems are control
systems where the control is discretized in time while the system evolves
continuously. Networked systems can be seen as analogous to sampled-data
control systems where the state space is discretized. In particular, we will develop
indirect methods based on the Pontryagin Maximum Principle to compute
optimal controls for these systems. We will find applications in medicine, ecology
and game theory.

Bio: Dr. Gaurav Dhar is a Research Scientist at the Michigan Tech Research
Institute. He defended his PhD in Applied Mathematics with a thesis on
Optimal Control Theory from the University of Limoges in 2020. He holds a
Bachelor's Degree in Mathematics and Biophysics from the Johns Hopkins
University. His research interests include Control Theory, Dynamical Systems,
Differential Equations as well as Machine Learning, Autonomy and Multiagent
systems.