Least square algorithm based on bias compensated principle for parameter estimation of canonical state space model

Liu, Longlong; Long, Zhen; Azar, Ahmad Taher; Zhu, Quanmin; Ibraheem, Ibraheem Kasim; Humaidi, Amjad J

Abstract

Due to the existence of system noise and unknown state variables, it is difficult to realize unbiased estimation with minimum variance for the parameter estimation of canonical state space model. This paper presents a new least squares estimator based on bias compensation principle to solve this problem, transforms canonical state space into the form suitable for the least square algorithm, introduces an augmented parameter vector and an auxiliary variable, derives parameter estimation formula based on noise compensation, realizes the unbiased estimation, and gives the specific algorithm. A simulation example is provided to verify the effectiveness of the estimator.

Citation

Liu, L., Long, Z., Azar, A. T., Zhu, Q., Ibraheem, I. K., & Humaidi, A. J. (2022). Least square algorithm based on bias compensated principle for parameter estimation of canonical state space model. Measurement and Control, 55(5-6), https://doi.org/10.1177/00202940211064179

• Journal Article Type: Article
• Acceptance Date: Oct 25, 2021
• Online Publication Date: Jun 1, 2022
• Publication Date: Jun 1, 2022
• Deposit Date: Jun 27, 2022
• Publicly Available Date: Jun 27, 2022
• Journal: Measurement and Control (United Kingdom)
• Print ISSN: 0020-2940
• Electronic ISSN: 2051-8730
• Publisher: SAGE Publications (UK and US)
• Peer Reviewed: Peer Reviewed
• Volume: 55
• Issue: 5-6
• DOI: https://doi.org/10.1177/00202940211064179
• Keywords: Applied Mathematics, Control and Optimization, Instrumentation
• Public URL: https://uwe-repository.worktribe.com/output/9645625
• Publisher URL: https://journals.sagepub.com/home/mac