Dynamic Parameter Identification of Mathematical Model of Lithium-Ion Battery Based on Least Square Method

Combined with the contents of the study-PSS low-pass link parameter identification. Least-squares method is selected. Using least-square method for PSS low-pass link mathematical model are also deduced. For the results, because of the mathematical model is solving nonlinear equations, cannot used by the Newton method directly. So we choose to use Newton iterations, with this feature, choose to use MATLAB software to solve the equation. Identification of the use of MATLAB software lags after the PSS parameters obtained recognition results compared with national standards, identifying and verifying the practicability.

Download Full-text

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

Applied Sciences ◽

10.3390/app9020324 ◽

2019 ◽

Vol 9 (2) ◽

pp. 324 ◽

Cited By ~ 3

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.

Download Full-text

Parameter identification of a lithium-ion battery based on the improved recursive least square algorithm

IET Power Electronics ◽

10.1049/iet-pel.2019.1589 ◽

2020 ◽

Vol 13 (12) ◽

pp. 2531-2537

Author(s):

Biying Ren ◽

Chenxue Xie ◽

Xiangdong Sun ◽

Qi Zhang ◽

Dan Yan

Keyword(s):

Parameter Identification ◽

Lithium Ion Battery ◽

Lithium Ion ◽

Least Square ◽

Recursive Least Square ◽

Recursive Least Square Algorithm

Download Full-text

Modeling and Dynamic Parameter Identification of the SCHUNK Powerball Robotic Arm

Volume 5C: 39th Mechanisms and Robotics Conference ◽

10.1115/detc2015-47703 ◽

2015 ◽

Cited By ~ 4

Author(s):

Amirhossein H. Memar ◽

Ehsan T. Esfahani

Keyword(s):

Parameter Identification ◽

Viscous Friction ◽

Dynamic Parameter ◽

Human Robot Interaction ◽

Least Square ◽

Robotic Arm ◽

Inverse Dynamic ◽

Compact Design ◽

Dynamic Parameter Identification ◽

Linear Least Square

This paper presents the modeling and dynamic parameter identification of the 6-DoF SCHUNK Powerball LWA 4P robotic arm. Precise positioning, zero backlash and compact design of the joints which integrate two perpendicular axes, make this robot ideal for service robotics applications and human-robot interaction. Due to the significant effect of the lubricant temperature on the behavior of viscous friction in the harmonic drives, a systematic procedure is developed to overcome this problem. A series of experiments have been conducted to model the friction at each joint, then the procedure of identification has been applied based on an inverse dynamic model and linear least-square techniques. Finally, a verification trajectory is executed by the robot to validate the estimated parameters of the system.

Download Full-text

Research on Modeling and Parameter Identification of Electro-Hydraulic Servo Loading System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.889-890.970 ◽

2014 ◽

Vol 889-890 ◽

pp. 970-977

Author(s):

Song Wang ◽

Chuan Gui Yang ◽

Fei Chen ◽

Zhao Jun Yang ◽

Zhuang Tan ◽

...

Keyword(s):

Mathematical Model ◽

Parameter Identification ◽

Test Bench ◽

Least Square Method ◽

Pso Algorithm ◽

Least Square ◽

Identification Accuracy ◽

Loading System ◽

Hydraulic Servo ◽

The Mathematical Model

In order to improve the mathematical models accuracy of the electro-hydraulic servo loading system from the high-speed motorized spindle reliability test bench. This paper establishes the mathematical model based on the dynamic characteristics of the test bench, then establishes discrete mathematical model of the system based on the Z-transform, and finally uses particle swarm optimization (PSO) algorithm to identify the parameters of the discrete model. Additionally, the least square method is applied to identify the parameters of the model for measuring the PSO algorithm parameter identification capability in our paper. The experimental results show that the mathematical model, identified by the PSO algorithm, can simulate the loading process very well under the strong interference signals, and the result is better than that gotten by the least square method, which proves that the PSO algorithm has high identification accuracy and better capability in parameters identification .

Download Full-text