Unknown piecewise constant parameters identification with exponential rate of convergence. (24th November 2022)

• Record Type: Journal Article
• Authors: Glushchenko, Anton; Lastochkin, Konstantin
• Is Part Of: International journal of adaptive control and signal processing. Volume 37:Number 1(2023)
• Page Start: 315
• Page End: 346
• Publication Date: 2022-11-24
• Subjects: extension and mixing -- finite excitation -- identification -- linear regression -- monotonicity -- piecewise constant parameters -- switching; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3533
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: The scope of this research is the identification of unknown piecewise constant parameters of linear regression equation under the finite excitation condition. Compared to the known methods, to make the computational burden lower, only one model to identify all switching states of the regression is used in the developed procedure with the following two‐fold contribution. First of all, we propose a new truly online estimation algorithm based on a well‐known DREM approach to detect switching time and preserve time alertness with adjustable detection delay. Second, despite the fact that a switching signal function is unknown, the adaptive law is derived that provides global exponential convergence of the regression parameters estimates to their true values in case the regressor is finitely exciting somewhere inside the time interval between two consecutive parameters switches. The robustness of the proposed identification procedure to the influence of external disturbances is analytically proved. Its effectiveness is demonstrated via numerical experiments, in which both abstract regressions and a second‐order plant model are used.