Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5000206 | Automatica | 2017 | 11 Pages |
Abstract
We present a method for robust input design for nonlinear state-space models. The method optimizes a scalar cost function of the Fisher information matrix over a set of marginal distributions of stationary processes. By using elements from graph theory we characterize such a set. Since the true system is unknown, the resulting optimization problem takes the uncertainty on the true value of the parameters into account. In addition, the required estimates of the information matrix are computed using particle methods, and the resulting problem is convex in the decision variables. Numerical examples illustrate the proposed technique by identifying models using the expectation-maximization algorithm.
Related Topics
Physical Sciences and Engineering
Engineering
Control and Systems Engineering
Authors
Patricio E. Valenzuela, Johan Dahlin, Cristian R. Rojas, Thomas B. Schön,