Abstract
In this paper, an iterative learning recursive least squares (ILRLS) identification method is developed by considering a class of repetitive systems. First, considering a repetitive discrete-time system corrupted by white noise, we present a linear time-varying data model to describe the input-output dynamic behavior of the system in iteration domain. On this basis, two ILRLS methods are proposed taking both white noises and colored noises into consideration. With an extensive analysis, the two proposed methods are shown applicable to repetitive nonlinear discrete-time systems owing to their data-driven nature by which no explicit models are required. The proposed ILRLS methods are executed pointwisely along the iteration direction, and they can also deal with time-varying uncertainties. The results are proved and verified by mathematical analysis along with simulations.
Highlights
System identification is of great importance in many engineering fields such as in chemical process [1], [2], power system [3], [4], biomedical system [5], [6], and so on
An iterative linear time-varying data model is derived for linear repetitive systems subject to white noise, and the linear time-varying data model based iterative learning recursive least squares (ILRLS) identifications algorithm is developed
Two linear time-varying data model based ILRLS identification methods are proposed for repetitive discrete-time systems with white noises and colored noises, respectively
Summary
System identification is of great importance in many engineering fields such as in chemical process [1], [2], power system [3], [4], biomedical system [5], [6], and so on. N. Lin et al.: LTV Data Model-Based ILRLS Identifications for Repetitive Systems learning RLS algorithm has been developed in [22], [23] for time-varying autoregressive moving average exogenous models. Reference [30] uses fuzzy neural modeling to identify the nonlinear time-varying plant All of these nonlinear system identification methods [28]–[30] are only conducted in time domain and little result has been reported for nonlinear iterative identification of repetitive systems. In view of the above considerations, we propose an iterative learning RLS (ILRLS) identification method for repetitive systems under a data-driven framework in this work. An iterative linear time-varying data model is derived for linear repetitive systems subject to white noise, and the linear time-varying data model based ILRLS identifications algorithm is developed.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
