Two recursive least squares parameter estimation algorithms for multirate multiple-input systems by using the auxiliary model

2012 ◽  
Vol 82 (5) ◽  
pp. 777-789 ◽  
Author(s):  
Lili Han ◽  
Fangxiang Wu ◽  
Jie Sheng ◽  
Feng Ding
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Recursive Least Squares ◽  
Auxiliary Model ◽  
Estimation Algorithms ◽  
Multiple Input

scholarly journals Least-Squares Based and Gradient Based Iterative Parameter Estimation Algorithms for a Class of Linear-in-Parameters Multiple-Input Single-Output Output Error Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Cheng Wang ◽  
Tao Tang ◽  
Dewang Chen
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Iterative Algorithm ◽  
Parameter Estimates ◽  
Iterative Search ◽  
Estimation Algorithms ◽  
Single Output ◽  
Multiple Input ◽  
Gradient Based ◽  
Simulation Results

The identification of a class of linear-in-parameters multiple-input single-output systems is considered. By using the iterative search, a least-squares based iterative algorithm and a gradient based iterative algorithm are proposed. A nonlinear example is used to verify the effectiveness of the algorithms, and the simulation results show that the least-squares based iterative algorithm can produce more accurate parameter estimates than the gradient based iterative algorithm.

Online parameter estimation and adaptive control with limited information in structures

2021 ◽  
pp. 107754632110191
Author(s):  
Fereidoun Amini ◽  
Elham Aghabarari
Keyword(s):  
Parameter Estimation ◽  
Adaptive Control ◽  
Least Squares ◽  
Control Algorithm ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Limited Information ◽  
Online Identification ◽  
Recursive Least Squares Method ◽  
Adaptive Control Algorithm

An online parameter estimation is important along with the adaptive control, that is, a time-dependent plant. This study uses both online identification and the simple adaptive control algorithm with velocity feedback. The recursive least squares method was used to identify the stiffness and damping parameters of the structure’s stories. Identification was carried out online without initial estimation and only by measuring the structural responses. The limited information regarding sensor measurements, parameter convergence, and the effects of the covariance matrix is examined. The integration of the applied online identification, the appropriate reference model selection in simple adaptive control, and adopting the proportional integral filter was used to limit the structural control response error. Some numerical examples are simulated to verify the ability of the proposed approach. Despite the limited information, the results show that the simultaneous use of online identification with the recursive least squares method and simple adaptive control algorithm improved the overall structural performance.

SYSTOLIC ALGORITHMS FOR RECURSIVE TOTAL LEAST SQUARES PARAMETER ESTIMATION AND MIXED RLS/RTLS PROBLEMS

1993 ◽  
Vol 04 (01) ◽  
pp. 55-68 ◽  
Author(s):  
MARC MOONEN
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Total Least Squares ◽  
Parallel Architectures ◽  
Least Squares Estimation ◽  
Recursive Least Squares ◽  
Recursive Algorithms ◽  
Least Squares Problems ◽  
Linearly Constrained ◽  
Systolic Algorithms

Total least squares parameter estimation is an alternative to least squares estimation though much less used in practice, partly due to the absence of efficient recursive algorithms or parallel architectures. Here it is shown how previously developed systolic algorithms/architectures for recursive least squares estimation can be used for recursive total least squares problems. Unconstrained as well as linearly constrained and "mixed RLS/RTLS" problems are considered.

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