Robust adaptive identification of linear time‐varying systems with modified least‐squares algorithm. Issue 25 (1st December 2016)

• Record Type: Journal Article
• Authors: Wu, Jinbo; Huang, Wenjian; Song, Yuchen; Zhang, Jinguo
• Is Part Of: Electronics letters. Volume 52:Issue 25(2016)
• Page Start: 2038
• Page End: 2040
• Publication Date: 2016-12-01
• Subjects: robust control -- adaptive control -- linear systems -- time‐varying systems -- estimation theory -- covariance analysis -- parameter estimation -- regression analysis; modified least‐squares algorithm -- forgetting factor -- linear time‐varying systems -- Euclidean norm -- estimation error -- bounded regressor -- persistent excitation condition -- bounded disturbances -- time‐varying parameters -- covariance update law -- robust adaptive identification law -- parameter estimation; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2016.2718
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : A modified least‐squares algorithm with forgetting factor is proposed and applied to linear time‐varying systems. The bound on the Euclidean norm of the estimation error is derived mathematically. When the bounded regressor of the linear time‐varying system satisfies persistent excitation condition, this new algorithm can attenuate the influences of bounded disturbances and the change ratio of time‐varying parameters effectively through extra term that is added to the covariance's update law. Contrary to the earlier proposed estimation laws, this algorithm does not require any prior knowledge of upper bounds on the parameters and the disturbances. Simulation results demonstrate the effectiveness of the identification law.