Dynamic parameter identification method derived from dynamic forgetting factor recursive least square method

2021 ◽  
Author(s):  
Wenhao Lv ◽  
Mei Liu ◽  
Jinming Liu ◽  
Xingchen Zhu ◽  
Zhenyu Zhang
Keyword(s):  
Parameter Identification ◽  
Least Square Method ◽  
Dynamic Parameter ◽  
Least Square ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Identification Method ◽  
Recursive Least Square Method ◽  
Dynamic Parameter Identification

scholarly journals Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO

2019 ◽  
Vol 9 (2) ◽  
pp. 324 ◽  
Author(s):  
Fusheng Zha ◽  
Wentao Sheng ◽  
Wei Guo ◽  
Shiyin Qiu ◽  
Jing Deng ◽  
...  
Keyword(s):  
Lower Extremity ◽  
Parameter Identification ◽  
Dynamic Parameter ◽  
Search Space ◽  
Least Square ◽  
Identification Accuracy ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Experimental Conditions ◽  
Dynamic Parameter Identification

The lower extremity exoskeleton is a device for auxiliary assistance of human movement. The interaction performance between the exoskeleton and the human is determined by the lower extremity exoskeleton’s controller. The performance of the controller is affected by the accuracy of the dynamic equation. Therefore, it is necessary to study the dynamic parameter identification of lower extremity exoskeleton. The existing dynamic parameter identification algorithms for lower extremity exoskeletons are generally based on Least Square (LS). There are some internal drawbacks, such as complicated experimental processes and low identification accuracy. A dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by Recursive Least Square (RLS) is developed in this investigation. The developed algorithm is named RLS-PSO. By defining the search space of PSO, RLS-PSO not only avoids the convergence of identified parameters to the local minima, but also improves the identification accuracy of exoskeleton dynamic parameters. Under the same experimental conditions, the identification accuracy of RLS-PSO, PSO and LS was quantitatively compared and analyzed. The results demonstrated that the identification accuracy of RLS-PSO is higher than that of LS and PSO.

scholarly journals Lithium battery parameter identification and SOC estimation based on dual-polarized model

2021 ◽  
Vol 300 ◽  
pp. 01013
Author(s):  
Hui Xia ◽  
Changlei Li ◽  
Yusang Xu ◽  
Xuehong Liu
Keyword(s):  
Lithium Battery ◽  
Estimation Error ◽  
Equivalent Circuit Model ◽  
Least Square Method ◽  
Base Station ◽  
Least Square ◽  
Model Parameters ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Recursive Least Square Method

An equivalent circuit model of dual polarization (DP) of lithium battery was established according to the application characteristics of lithium battery under the standby condition of 5G base station. On the basis of the model, recursive least square method with forgetting factor (RLS) was used to identify the model parameters. Finally, the Unscented Kalman filtering (UKF) was used to estimate the SOC of lithium battery in real time with the identified model parameters. The simulation and experimental results showed that the combined estimation using recursive least square method with forgetting factor (RLS) and UKF could greatly improve the estimation accuracy of lithium battery SOC, reduce the estimation error, and further verify the accuracy and effectiveness of the whole modeling.

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