Frequency Response Function Identification from Incomplete Data: A Wavelet-based Approach. Issue 37 (2022)

• Record Type: Journal Article
• Authors: Dirkx, Nic; Tiels, Koen; Oomen, Tom
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 37(2022)
• Page Start: 439
• Page End: 444
• Publication Date: 2022
• Subjects: Frequency domain identification -- non-parametric methods -- missing data -- transient estimation -- linear systems; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.11.222
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Frequency Response Function (FRF) identification plays a crucial role in the design, the control, and the analysis of complex dynamical systems, including thermal and motion systems. Especially for applications that require long measurements, missing data samples, e.g., due to interruptions in the data transmission or sensor failure, often occur. The aim of this paper is to accurately identify nonparametric FRF models of periodically excited systems from noisy output measurements with missing samples. The presented method employs a wavelet-based transformation to address the identification problem in the time-frequency plane. A simulation example confirms that the developed techniques produce accurate estimates, even when many samples are missing.