scholarly journals A New Physical Parameter Identification Method for Two-Axis On-Road Vehicles: Simulation and Experiment

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Minyi Zheng ◽  
Peng Peng ◽  
Bangji Zhang ◽  
Nong Zhang ◽  
Lifu Wang ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Parameter Identification ◽  
Physical Parameter ◽  
Mass Matrix ◽  
Estimation Method ◽  
Least Square Method ◽  
Least Square ◽  
Percentage Error ◽  
Physical Parameters ◽  
Identification Method

A new physical parameter identification method for two-axis on-road vehicle is presented. The modal parameters of vehicle are identified by using the State Variable Method. To make it possible to determine the matricesM,C, andKof the vehicle, a known mass matrixΔMis designed to add into the vehicle in order to increase the number of equations ensuring that the number of equations is more than the one of unknowns. Therefore, the physical parameters of vehicle can be estimated by using the least square method. To validate the presented method, a numerical simulation example and an experiment example are given in this paper. The numerical simulation example shows that the largest of absolute value of percentage error is 1.493%. In the experiment example, a school bus is employed in study for the parameter identification. The simulation result from full-car model with the estimated physical parameters is compared with the test result. The agreement between the simulation and the test proves the effectiveness of the proposed estimation method.

scholarly journals The Parameter Estimation of Double Exponential Pulse Based on a Least Infinity Norm Fitting Method

2021 ◽  
Vol 2083 (4) ◽  
pp. 042002
Author(s):  
Yuewu Shi ◽  
Wei Wang ◽  
Zhizhen Zhu ◽  
Xin Nie
Keyword(s):  
Estimation Error ◽  
Estimation Method ◽  
Maximum Amplitude ◽  
Least Square Method ◽  
Least Square ◽  
Physical Parameters ◽  
Infinity Norm ◽  
Fitting Method ◽  
Double Exponential ◽  
Exponential Pulse

Abstract This paper presents an estimation method of double exponential pulse (DEP) between the physical parameters rise time (t r), full width at half maximum amplitude (t FWHM) and the mathematical parameters α, β. A newly fitting method based on the least infinity norm criterion is proposed to deal with the estimation problem of DEP. The calculation process and equation of parameters of this method is proposed based on an m-th-order polynomial fitting model. This estimation method is compared with the least square method by the same data and fitting function. The results show that the maximum estimation error of parameters of double exponential pulse obtained by the least infinity norm method is 1.5 %.

Structural Parameter Identification Based on Equivalent Single Degree Systems

2010 ◽  
Vol 450 ◽  
pp. 510-513
Author(s):  
Yan Wei Wang ◽  
Zi Fa Wang ◽  
Rui Zhi Wen
Keyword(s):  
Numerical Simulation ◽  
Differential Evolution ◽  
Parameter Identification ◽  
Physical Parameter ◽  
Optimization Algorithm ◽  
New Method ◽  
Structural Parameter ◽  
Physical Parameters ◽  
Single Degree

In order to solve the problems of optimization algorithm used to identify the physical parameters of structures, a new method based on a series of equivalent single degree systems is proposed in this paper. The key idea of the method is that a multi-degree system can be represented by a series of single degree systems that can be identified one by one to perform the identification of the whole system. This method can not only decrease the dimensions of optimization algorithm, but also reduce the amount of estimation work in searching for the bound of parameters, and at the same time improve the identification results when parameters might suddenly change. In the numerical simulation of the physical parameter identification of a multi-degree system, Differential evolution is one of the optimization algorithm methods which are used to identify a series of equivalent single degree systems instead of the multi-degree system they represent, and the identification results prove that the method proposed in this paper is valid.

Observer-Based Identification of Nonlinear System Parameters

1995 ◽  
Vol 117 (2) ◽  
pp. 175-182 ◽  
Author(s):  
Kyongsu Yi ◽  
Karl Hedrick
Keyword(s):  
Nonlinear System ◽  
Parameter Identification ◽  
Least Square Method ◽  
Nonlinear Damping ◽  
Point Of View ◽  
Least Square ◽  
Parameter Estimates ◽  
Identification Method ◽  
Pitch Moment ◽  
Second Order Systems

This paper deals with an observer-based nonlinear system parameter identification method utilizing repetitive excitation. Although methods for physical parameter identification of both linear and nonlinear systems are already available, they are not attractive from a practical point of view since the methods assume that all the system, x, and the system input are available. The proposed method is based on a “sliding observer” and a least-square method. A sufficient condition for the convergence of the parameter estimates is provided in the case of “Lipschitz” nonlinear second-order systems. The observer is used to estimate signals which are difficult or expensive to measure. Using the estimated states of the system with repetitive excitation, the parameter estimates are obtained. The observer based identification method has been tested on a half car simulation and used to identify the parameters of a half car suspension test rig. The estimates of nonlinear damping coefficients of a vehicle suspension, suspension stiffness, pitch moment inertia, equivalent sprung mass, and unsprung mass are obtained by the proposed method. Simulation and experimental results show that the identifier estimates the vehicle parameters accurately. The proposed identifier will be useful for parameter identification of actual vehicles since vehicle parameters can be identified only using vehicle excitation tests rather than component testing.

Research on Generator Excitation Parameter Identification - PSS Low-Pass Link Parameter Identification

2013 ◽  
Vol 805-806 ◽  
pp. 716-720
Author(s):  
Tao Xu ◽  
Tian Long Shao ◽  
Dong Fang Zhang
Keyword(s):  
Mathematical Model ◽  
Parameter Identification ◽  
Least Squares Method ◽  
Least Square Method ◽  
Least Square ◽  
National Standards ◽  
Matlab Software ◽  
Low Pass ◽  
Newton Iterations ◽  
The Mathematical Model

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.

scholarly journals A robust vector autoregressive model for forecasting economic growth in Malaysia

2018 ◽  
Vol 14 (3) ◽  
pp. 382-385
Author(s):  
Azme Khamis ◽  
Nur Azreen Abdul Razak ◽  
Mohd Asrul Affendi Abdullah
Keyword(s):  
Economic Growth ◽  
Least Square Method ◽  
Economic Indicator ◽  
Performance Measure ◽  
Least Square ◽  
Economic Indicators ◽  
Percentage Error ◽  
Monthly Basis ◽  
Vector Autoregressive ◽  
Auto Regressive

Economic indicator measures how solid or strong an economy of a country is. Basically, economic growth can be measured by using the economic indicators as they give an account of the quality or shortcoming of an economy. Vector Auto-regressive (VAR) method is commonly useful in forecasting the economic growth involving a bounteous of economic indicators. However, problems arise when its parameters are estimated using least square method which is very sensitive to the outliers existence. Thus, the aim of this study is to propose the best method in dealing with the outliers data so that the forecasting result is not biased. Data used in this study are the economic indicators monthly basis starting from January 1998 to January 2016. Two methods are considered, which are filtering technique via least median square (LMS), least trimmed square (LTS), least quartile difference (LQD) and imputation technique via mean and median. Using the mean absolute percentage error (MAPE) as the forecasting performance measure, this study concludes that Robust VAR with LQD filtering is a more appropriate model compare to others model. 

Cutting Force Empirical Model of High-Speed Precision Hard Cutting GCr15

2009 ◽  
Vol 69-70 ◽  
pp. 301-305
Author(s):  
Jing Shu Hu ◽  
Yuan Sheng Zhai ◽  
Fu Gang Yan ◽  
Yu Fu Li ◽  
Xian Li Liu
Keyword(s):  
Cutting Force ◽  
Empirical Model ◽  
High Speed ◽  
Orthogonal Design ◽  
Cutting Speed ◽  
Least Square Method ◽  
Cutting Parameters ◽  
Least Square ◽  
Physical Parameters ◽  
Hard Cutting

In the cutting process, cutting force is one of the important physical parameters, which affects the generation of cutting heat, tool life and surface precision of workpiece directly. In this paper an orthogonal design of experiment and subsequent data is analyzed using high speed finish hard cutting GCr15 whose hardness is 65HRC. Cutting speed is 200-400m/min, to study the influence of cutting parameters on cutting force, cutting force empirical model has obtained from least square method.

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