Recursive identification of time-varying systems: Self-tuning and matrix RLS algorithms

• Published in: Systems & control letters Vol. 66; pp. 104 - 110
• Main Authors: Li, Jianshu, Zheng, Yuanjin, Lin, Zhiping
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2014
• Subjects: Adaptive algorithms; Algorithms; Computer simulation; Control systems; Least squares method; Matrix forgetting factor RLS algorithm; Optimization; Recursive; Recursive identification; Regularization; RLS algorithm; Self-tuning; Time-varying system; RLS algorithm; Recursive identification; Matrix forgetting factor RLS algorithm; Self-tuning; Time-varying system
• ISSN: 0167-6911; 1872-7956
• DOI: 10.1016/j.sysconle.2014.01.004

In this paper, a new parallel adaptive self-tuning recursive least squares (RLS) algorithm for time-varying system identification is first developed. Regularization of the estimation covariance matrix is included to mitigate the effect of non-persisting excitation. The desirable forgetting factor can be self-tuning estimated in both non-regularization and regularization cases. We then propose a new matrix forgetting factor RLS algorithm as an extension of the conventional RLS algorithm and derive the optimal matrix forgetting factor under some reasonable assumptions. Simulations are given which demonstrate that the performance of the proposed self-tuning and matrix RLS algorithms compare favorably with two improved RLS algorithms recently proposed in the literature.