Dr. David Copp
Postdoctoral Associate, Sandia National Laboratories
Friday, March 9, 2018, at 3 PM
Woodward Hall, Room 147
Talk abstract: In this talk, we discuss the use of control and optimization for solving sophisticated engineering problems, with motivating examples in bioengineering and energy systems. Model predictive control is a particularly popular online optimal control approach due to its ability to explicitly handle hard state and input constraints. We introduce an output-feedback approach to model predictive control for discrete-time nonlinear systems. This approach combines state estimation and control into a single min-max optimization; specifically, a criterion that involves finite forward and backward horizons is minimized with respect to control input variables and is maximized with respect to the unknown initial state as well as disturbance and measurement noise variables. Lastly, we discuss the advantages of using this combined optimal estimation and control approach in applications including the coordination of unmanned aerial vehicles, feedback control of an artificial pancreas, and potential applications in power and energy systems.