Convergence Analysis of the Hierarchical Least Squares Algorithm for Bilinear-in-Parameter Systems

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• 作者：Xuehai Wang ; Feng Ding ; Fuad E. Alsaadi…
• 关键词：Parameter estimation ; Recursive identification ; Least squares ; Nonlinear system ; Martingale convergence theorem
• 刊名：Circuits, Systems, and Signal Processing
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：35
• 期：12
• 页码：4307-4330
• 全文大小：951 KB
• 刊物类别：Engineering
• 刊物主题：Electronic and Computer Engineering
  
• 出版者：Birkh盲user Boston
• ISSN：1531-5878
• 卷排序：35

文摘

This paper studies the convergence of the hierarchical identification algorithm for bilinear-in-parameter systems. By replacing the unknown variables in the information vector with their estimates, a hierarchical least squares algorithm is derived based on the model decomposition. The proposed algorithm has higher computational efficiency than the over-parameterization model-based recursive least squares algorithm. The performance analysis shows that the parameter estimation errors converge to zero under persistent excitation conditions. The effectiveness of the proposed algorithm is verified by simulation examples.