On Optimal Input Design in System Identification for Control
Abstract:
The quality of an estimated model should be related to the specifications of the intended application. A classical approach is to study the "size" of the asymptotic covariance matrix (the inverse of the Fisher information matrix) of the corresponding parameter vector estimate. In many cases it is possible to design and implement external excitation signals, e.g. pilot signals in communications systems or input signals in control applications. The objective of this seminar is to present some recent advances in optimal experiment design for system identification with a certain application in mind. The idea is to minimize experimental costs (e.g. the energy of the excitation signal), while guarantying that the estimated model with a given probability satisfies the specifications of the application. This will result in a convex optimization problem, where the optimal solution should reveal system properties important for the application while hiding irrelevant dynamics. Simple Finite Impulse Response (FIR) examples will be used to illustrate the basic ideas.
This seminar is based on joint work with Mariette Annergren, Håkan Hjalmarsson, Christian Larsson and Cristian Rojas, KTH.