DAE

Abstract

In this talk we present a framework for applications-oriented experiment design for dynamic systems. The idea is to generate a design such that certain performance criteria of the application are satisfied with high probability. We discuss how to approximate this problem by a convex optimization problem and how to address Achilles' heel of optimal experiment design, i.e., that the optimal design depends on the true system. We also elaborate on how the cost of an identification experiment is related to the performance requirements of the application and the importance of experiment design in reduced order modeling. We illustrate the methods on some problems from control and systems theories.