Further Research on Load Modeling and Parameter Identification based on Online Measured Data

2012 ◽  
Author(s):  
Yuan Yao ◽  
Qian Ai ◽  
Xing He ◽  
Da Xie ◽  
Zheng Yan ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Measured Data ◽  
Load Modeling

scholarly journals ONLINE PARAMETER IDENTIFICATION OF RICE TRANSPLANTER MODEL BASED ON IPSO-EKF ALGORITHM

2020 ◽  
Vol 61 (2) ◽  
pp. 25-34 ◽  
Author(s):  
Yibo Li ◽  
Hang Li ◽  
Xiaonan Guo
Keyword(s):  
Parameter Identification ◽  
Measured Data ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Output Data ◽  
Swarm Optimization ◽  
Model Based ◽  
Simulation Results ◽  
Filtering Effect ◽  
Ipso Algorithm

In order to improve the accuracy of rice transplanter model parameters, an online parameter identification algorithm for the rice transplanter model based on improved particle swarm optimization (IPSO) algorithm and extended Kalman filter (EKF) algorithm was proposed. The dynamic model of the rice transplanter was established to determine the model parameters of the rice transplanter. Aiming at the problem that the noise matrices in EKF algorithm were difficult to select and affected the best filtering effect, the proposed algorithm used the IPSO algorithm to optimize the noise matrices of the EKF algorithm in offline state. According to the actual vehicle tests, the IPSO-EKF was used to identify the cornering stiffness of the front and rear tires online, and the identified cornering stiffness value was substituted into the model to calculate the output data and was compared with the measured data. The simulation results showed that the accuracy of parameter identification for the rice transplanter model based on the IPSO-EKF algorithm was improved, and established an accurate rice transplanter model.

Development of the Power Load Modeling System with Denoising and Parameter Identification

2013 ◽  
Vol 805-806 ◽  
pp. 712-715
Author(s):  
Li Di Wang ◽  
Qing Ying Ge ◽  
Zhe Li ◽  
Tai Gang Nian
Keyword(s):  
Parameter Identification ◽  
Dynamic Model ◽  
Variable Load ◽  
Signal Acquisition ◽  
Load Modeling ◽  
Load Model ◽  
Process Measurement ◽  
Power Load ◽  
Measurement Signal ◽  
Zip Model

The power load modeling system is designed with denoising and parameter identification. This system consists of signal acquisition, signal preprocessing, parameter identification, different load modeling methods such as ZIP model and Dynamic modeling. Original signal can be read from Excel file, which is the simulated signal or measurement signal. Then some kinds of denoising methods can be selected, which are mean filtering, medial filtering and wavelet denoising. After being denoised, the load signal is suitable for the parameter identification process. ZIP model is used to simulate the static load model, and the dynamic model is used to simulate the dynamic load model which is changeable during different periods. With the parameter identification and simulation process, measurement power load signal is used in the experiment, the dynamic model is more suitable for the variable load voltage features description.

Structural Parameter Identification Based on Dynamic Response Sensitivity with Non-Zero Initial Conditions

2014 ◽  
Vol 488-489 ◽  
pp. 403-406
Author(s):  
Kun Zhang
Keyword(s):  
Dynamic Response ◽  
Parameter Identification ◽  
Initial Conditions ◽  
Initial Period ◽  
Optimal Number ◽  
Measured Data ◽  
Structural Parameter ◽  
Time Interval ◽  
Response Sensitivity ◽  
Negative Effect

The effect of non-zero initial conditions of measured structural responses on the structural parameter identification methods based on dynamic response sensitivity is investigated and an effective method is proposed to eliminate the negative effect. A twelve-story shear building structure is studied for validating the proposed method. Numerical simulation results show that the non-zero initial conditions of measured data bring larger identified errors in the dynamic response sensitivity methods. To overleap some measured data points in the initial period of the time interval is an effective method for eliminating the identified errors caused by non-zero initial conditions. The optimal number of overleaped data can be determined from the attenuation trend of the 1st-order free modal vibration of the structure.

Parameter Identification of Elastic Modulus of Rock Based on Blasting Waves in Tunnel Excavation

2011 ◽  
Vol 403-408 ◽  
pp. 75-79
Author(s):  
Yuto Motoyama ◽  
Mutsuto Kawahara
Keyword(s):  
Elastic Modulus ◽  
Parameter Identification ◽  
Elastic Moduli ◽  
Adjoint Equation ◽  
Measured Data ◽  
Blasting Vibration ◽  
Tunnel Excavation ◽  
First Order ◽  
Identification Method ◽  
Identification Technique

The objective of this research is to present an identification method for elastic moduli of ground rock, through the first-order adjoint equation method using the measurements of the blasting vibration in tunnel excavation. Parameter identification is a minimization problem of the square sum of discrepancy between the computed and observed velocities. For the identification of these parameters, the magnitudes of the blasting force should be identified beforehand. In this study, propagation of an elastic wave is assumed because the amplitude of such a wave is infinitesimal. After the identification of the blasting force, the elastic moduli of three layers are identified simultaneously. We assume that the damping of vibration is linear. By applying the identification technique at the Ohyorogi tunnel site, we verify that the method is useful for tunnel excavation. Using measured data from actual tunnel excavation sites, the numerical identification method presented in this paper is shown to be useful for practical tunnel excavation.

Parameter Identification for the Dynamic Load Modeling Based on Denoising Method of the Measurement Data

2011 ◽  
Vol 217-218 ◽  
pp. 907-910
Author(s):  
Li Di Wang ◽  
Jiang Feng Tang ◽  
Jun Sheng Shi
Keyword(s):  
Parameter Identification ◽  
Dynamic Load ◽  
Measurement Data ◽  
Wavelet Denoising ◽  
Load Modeling ◽  
Denoising Method ◽  
Specific Approach ◽  
Identification Process ◽  
Filtering Method ◽  
Mean Filtering

Different denoising methods are used in parameter identification for the dynamic load modeling and the specific approach is proposed. The effects of different denoising methods including mean filtering, medial filtering and wavelet denoising are discussed. Mean filtering method is not helpful to contain the step changes of the measurement voltage, thus is unsuitable for the parameter identification process. Medial filtering method and wavelet denoising methods are suitable for the parameter identification in dynamic load modeling. Furthermore, experiment results based on the measurement data show that the wavelet denoising method is more efficient in some aspects such as the accuracy of identification and SSE.

Sign in / Sign up

Export Citation Format

Share Document