State Estimation for Atomic Clocks System under Non-stationary Noise Using Recursive Least-squares Method with Vector-type Variable Forgetting Factor

2020 ◽  
Vol 56 (2) ◽  
pp. 45-50
Author(s):  
Shunichiro MABUCHI ◽  
Masato HIRANO ◽  
Shuichi ADACHI ◽  
Tetsuya IDO ◽  
Yuko HANADO
Keyword(s):  
Least Squares ◽  
State Estimation ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Forgetting Factor ◽  
Stationary Noise ◽  
Vector Type ◽  
Type Variable ◽  
Recursive Least Squares Method ◽  
Variable Forgetting Factor

scholarly journals A Method for Parameter Identification of Combined Integrating Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhiping Fan ◽  
Zhengyun Ren ◽  
Angang Chen ◽  
Xue Feng ◽  
Wenbin Wang
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Industrial Process ◽  
Smooth Curve ◽  
Identification Accuracy ◽  
Recursive Least Squares ◽  
Experimental Simulation ◽  
Forgetting Factor ◽  
Novel Structure ◽  
Recursive Least Squares Method

This manuscript presents a novel structure of combined integration in the process industry and proposes an efficient method for identifying its parameters. Combined integrating processes are delay-time processes that widely exist in the industry. Conventional identification methods have a low-identification accuracy, a large vibration amplitude of the identification curve, and a poor effect for this kind of process. In this paper, a new variable forgetting factor recursive least squares method was adopted to ameliorate this problem. The method could quickly track the mutation of the ideal parameters of the process and accurately identify which of the parameters has high precision, small oscillation, and a smooth curve. The simulation results indicate that the proposed method is a significant improvement compared to the ordinary recursive least squares method and the recursive least squares with a fixed forgetting factor method, and a concise program can be verified. The experimental simulation based on the actual cut tobacco rebaking industrial process shows that the proposed method has improved identification precision and the best following effect.

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.

Sign in / Sign up

Export Citation Format

Share Document