Auxiliary model maximum likelihood gradient-based iterative identification for feedback nonlinear systems

被引:17
|
作者
Liu, Lijuan [1 ]
Li, Fu [1 ]
Ma, Junxia [2 ]
Xia, Huafeng [3 ]
机构
[1] Wuxi Univ, Coll Internet Things Engn, Wuxi 214105, Peoples R China
[2] Jiangnan Univ, Sch Internet Things Engn, Wuxi, Peoples R China
[3] Taizhou Univ, Taizhou Elect Power Convers & Control Engn Technol, Taizhou, Peoples R China
来源
OPTIMAL CONTROL APPLICATIONS & METHODS | 2024年 / 45卷 / 05期
关键词
feedback nonlinear system; gradient search; iterative identification; maximum likelihood; PARAMETER-ESTIMATION ALGORITHM; PERFORMANCE ANALYSIS; FAULT-DIAGNOSIS; STATE; OPTIMIZATION; TRACKING; DELAY;
D O I
10.1002/oca.3158
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This article considers the iterative identification problems for a class of feedback nonlinear systems with moving average noise. The model contains both the dynamic linear module and the static nonlinear module, which brings challenges to the identification. By utilizing the key term separation technique, the unknown parameters from both linear and nonlinear modules are included in a parameter vector. Furthermore, an auxiliary model maximum likelihood gradient-based iterative algorithm is derived to estimate the unknown parameters. In addition, an auxiliary model maximum likelihood stochastic gradient algorithm is derived as a comparison. The numerical simulation results indicate that the auxiliary model maximum likelihood gradient-based iterative algorithm can effectively estimate the parameters of the feedback nonlinear systems and get more accurate parameter estimates than the auxiliary model maximum likelihood stochastic gradient algorithm. Auxiliary model maximum likelihood gradient-based iterative indentification for feedback nonlinear systems. The AM-ML-GI algorithm has faster convergence speed and higher parameter estimation accuracy compared with the AM-ML-SG algorithm. image
引用
收藏
页码:2346 / 2363
页数:18
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