Decomposition‐based multiinnovation gradient identification algorithms for a special bilinear system based on its input‐output representation. (22nd March 2020)

• Record Type: Journal Article
• Authors: Wang, Longjin; Ji, Yan; Yang, Hualin; Xu, Ling
• Is Part Of: International journal of robust and nonlinear control. Volume 30:Number 9(2020)
• Page Start: 3607
• Page End: 3623
• Publication Date: 2020-03-22
• Subjects: bilinear system -- hierarchical identification -- multiinnovation -- parameter estimation -- stochastic gradient; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.4959
• Languages: English
• ISSNs: 1049-8923

Abstract

Summary: This article considers the parameter estimation for a special bilinear system with colored noise. Its input‐output representation is derived by eliminating the state variables in the bilinear system. Based on the input‐output representation of the bilinear system, a multiinnovation generalized extended stochastic gradient (MI‐GESG) algorithm is proposed by using the multiinnovation identification theory. Furthermore, a decomposition‐based multiinnovation (ie, hierarchical multiinnovation) generalized extended stochastic gradient identification (H‐MI‐GESG) algorithm is derived to enhance the parameter estimation accuracy by using the hierarchical identification principle, and a GESG algorithm is presented for comparison. Compared with the existing identification algorithms for the bilinear system, the proposed MI‐GESG and H‐MI‐GESG algorithms can generate more accurate parameter estimation. Finally, a simulation example is provided to verify the effectiveness of the proposed algorithms.