Decomposition‐based multiinnovation gradient identification algorithms for a special bilinear system based on its input‐output representation

• Published in: International journal of robust and nonlinear control Vol. 30; no. 9; pp. 3607 - 3623
• Main Authors: Wang, Longjin, Ji, Yan, Yang, Hualin, Xu, Ling
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.06.2020
• Subjects: Algorithms; bilinear system; Computer simulation; Decomposition; hierarchical identification; Identification; multiinnovation; Parameter estimation; Parameter identification; Representations; State variable; stochastic gradient
• ISSN: 1049-8923; 1099-1239
• DOI: 10.1002/rnc.4959

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.