L-two-optimal identification of errors-in-variables models: a frequency- domain approach

• 作 者: Lihui GENG Deyun XIAO Tao ZHANG Jingyan SONG
• 机 构: Department of Automation, Tsinghua University, Beijing 100084, China
• 出 处: 控制理论与应用（英文版）
• 发表日期: 2011年4期
• 关键词: Errors-in-variables model Normalized coprime factor model v-gap metric Linear ma

This paper proposes an L-two-optimal identification approach to cope with errors-in-variables model (EIVM) identification.With normalized coprime factor model (NCFM) representations,L-two-optimal approximate models are derived from the framework of an EIVM according to the kel and image representations of related signals.Based on the optimal approximate models,the v-gap metric is employed as a minimization criterion to optimize the parameters of a system model,and thus the resulting optimization problem can be solved by linear matrix inequalities (LMIs).In terms of the optimized system model,the noise model (NM) can be readily obtained by right multiplication of an inner.Compared with other EIVM identification methods,the proposed one has a wider scope of applications because the statistical properties of disturbing noises are not demanded.It is also capable of giving identifiability.Finally,a numerical simulation is used to verify the effectiveness of the proposed method.