文章基本信息

标题：An alternating optimization method for switched linear systems identification
本地全文：下载
作者：Federico Bianchi ; Alessandro Falsone ; Luigi Piroddi 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：1071-1076
DOI：10.1016/j.ifacol.2020.12.1297
语种：English
出版社：Elsevier
摘要：AbstractThe identification of switched systems involves solving a mixed-integer optimization problem to determine the parameters of each mode dynamics (continuous part) and assign the data samples to the modes (discrete part), so as to minimize a cost criterion measuring the quality of the model on a set of input/output data collected from the system. Oftentimes, some a priori information on the switching mechanism is available, e.g., in the form of a minimum dwell time. This information can be encoded in a suitable constraint and included in the optimization problem, but this introduces a coupling between the discrete and continuous optimization variables that makes the problem harder to solve. In this paper, we propose an iterative approach to the identification of switched systems that alternates a minimization step with respect to the continuous parameters of the modes, and a minimization step with respect to the discrete variables defining the sample-mode mapping. Constraints originating from prior knowledge on the switching mechanism are taken into account after the (unconstrained) discrete optimization step through a post-processing phase. These three phases are repeated until a stopping criterion is met. A comparative numerical analysis of the proposed method shows its improved performance with respect to competitive approaches in the literature.
关键词：KeywordsModel identificationSwitched systemsHybrid systems