On robust input design for nonlinear dynamical models

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• 作者：Patricio E. Valenzuela^a ; ^pva@kth.seAuthor Vitae ; Johan Dahlin^b ; ^{liu@johandahlin.com}Author Vitae ; Cristian R. Rojas^a ; ^crro@kth.seAuthor Vitae ; Thomas B. Schö ; n^b ; ^{thomas.schon@it.uu.se}Author Vitae
• 关键词：System identification ; Input design ; Particle filter ; Nonlinear systems
• 刊名：Automatica
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：77
• 期：Complete
• 页码：268-278
• 全文大小：6322 K
• 卷排序：77

文摘

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.