Computational and Applied Mathematics seminar

Abstract: The inverse linear-quadratic optimal control problem is a system identification problem whose aim is to recover the quadratic cost function and hence the closed-loop system matrices based on observations of optimal trajectories. The inverse optimal control framework not only offers a powerful framework to model and predict expert behaviors.

In this talk, I will introduce a couple of inverse optimal control formulations. Identifiability analysis for each formulation would be presented. With the persistent excitation assumptions, we can further show the presented algorithms are statistically consistent. In the end, inverse optimal control algorithms are applied in control algorithm design for rehabilitation robot.