A novel recursive approach for online identification of continuous‐time switched nonlinear systems. (22nd July 2021)

• Record Type: Journal Article
• Authors: Du, Yingwei; Liu, Fangzhou; Qiu, Jianbin; Buss, Martin
• Is Part Of: International journal of robust and nonlinear control. Volume 31:Number 15(2021)
• Page Start: 7546
• Page End: 7565
• Publication Date: 2021-07-22
• Subjects: integral concurrent learning -- mode recognition -- online identification -- switched nonlinear system; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5702
• Languages: English
• ISSNs: 1049-8923

Abstract

Abstract: This article focuses on the identification of switched nonlinear systems, which are characterized as a collection of nonlinear dynamical systems. Each nonlinear subsystem is activated by a discrete‐valued variable (switching signal). Specifically, we consider the continuous‐time switched nonlinear systems in the state‐space form in our article. The identification of switched nonlinear systems amounts to simultaneous estimation of the switching signal and the nonlinear dynamic subsystems via all measured state‐input vectors. However, the problem is challenging and generally requires a large computational complexity to be solved. In this article, we propose a novel online approach to address the identification problem of switched nonlinear systems, which is capable to handle the measured state‐input vectors in sequence. In particular, the principle used for estimating the switching signal is developed based on the subspace method. Subsequently, the integral concurrent learning identifier is extended to identify the dynamics of each subsystem recursively. The effectiveness of the proposed identification approach is demonstrated via simulation results.