Robust least square estimation of the CRS A465 robot arm’s dynamic model parameters

2012 ◽  
Vol 4 (3) ◽  
Author(s):  
Azeddien Kinsheel
Keyword(s):  
Dynamic Model ◽  
Least Square ◽  
Model Parameters ◽  
Least Square Estimation

A parameter identification method for robot dynamic models using a balancing mechanism

Robotica ◽  
1989 ◽  
Vol 7 (4) ◽  
pp. 327-337 ◽  
Author(s):  
T. G. Lim ◽  
H. S. Cho ◽  
W. K. Chung
Keyword(s):  
Dynamic Model ◽  
Dynamic Models ◽  
Least Square ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Robot Dynamics ◽  
Dynamic Parameters ◽  
Joint Torques ◽  
Motion Data ◽  
Identification Method

SUMMARYAccurate modeling of robot dynamics is a prerequisite for the design of model-based control schemes and enhancement of the performance of the robot. The dynamic parameters associated with a pseudo-inertia matrix are often difficult to identify accurately because the inertia torques are small in comparison to gravity loadings, thus creating signal processing problem. The identification method presented in this paper utilizes a balancing mechanism which increases the estimation accuracy of the dynamic parameters. The balancing mechanism has the effect of amplifying the inertia-related torque signal by eliminating gravity loadings acting on the robot joints. A series of motion data were experimentally obtained through sequential test steps. By incorporating the measured information about joint torques, angular positions, velocities and accelerations the least square algorithm was used to identify the dynamic parameters. The estimated values were converted to those of the original robot model to obtain its dynamic model parameters. The identified robot dynamic model was shown to be accurate enough to predict the actual robot motions.

Parameter Identification Research Based on ARX Model

2012 ◽  
Vol 190-191 ◽  
pp. 292-296
Author(s):  
Huai Yuan Liu ◽  
Jian Hua He ◽  
Song Chen
Keyword(s):  
System Identification ◽  
Programming Model ◽  
System Modeling ◽  
Estimation Method ◽  
Information Criterion ◽  
Least Square ◽  
Model Parameters ◽  
Least Square Estimation ◽  
Model Order ◽  
Arx Model

Based on the principle of the system identification, combined Simulink with System Identification Toolbox from MATLAB, the least square estimation method is selected to establish a system of ARX model, and Akaike Information Criterion (AIC) was used in the identification of model order, compared with the original model to study the fitting accuracy, and the validity of the model is examined by residual analysis. This approach overcomes the disadvantages of the complexity and difficulty in traditional programming model. Compared to other program identification method, it has a short modeling time, and it is clear, reliable, intuitive visual, good scalability. Furthermore, the model parameters, result and system can be easily modified, assessed and verified. This method of system modeling and simulation can be used for reference to aerospace and other fields.

Linear parameter variant modeling and parameter identification of a cable-driven micromanipulator for surgical robot

2018 ◽  
Vol 233 (5) ◽  
pp. 1828-1840 ◽  
Author(s):  
Wenjie Wang ◽  
Lingtao Yu ◽  
Jing Yang
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Model ◽  
Joint Angle ◽  
Linear Time ◽  
Surgical Robot ◽  
Least Square ◽  
Feedback Signal ◽  
Model Parameters ◽  
Linear Parameter ◽  
Nonlinear Dynamic Model

This paper proposes a novel cable-driven micromanipulator for surgical robots. A single-joint principle prototype for surgical robot micromanipulator was manufactured to test the proposed design. Elasticity and friction were assessed to establish a joint angle estimator; estimator parameters were obtained by a combination of least square method and genetic algorithm. Angle closed-loop control was performed by considering the joint angle estimator output as the feedback signal. A nonlinear dynamic model was established in the state-space and described as a linear parameter variant model. The dynamic model parameters were determined via nonlinear modeling method, linear time invariant interpolator, and genetic algorithm. The angle estimator performs well and the linear parameter variant model efficiently estimates the micromanipulator’s behavior. The results presented here provide a workable foundation for surgical robot micromanipulator force estimation and control.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

High-Accuracy Fundamental Frequency Detection in Power System

2011 ◽  
Vol 383-390 ◽  
pp. 4962-4966
Author(s):  
Ling Li ◽  
Guo Bin Jin ◽  
Shao Ping Huang ◽  
Xiao Peng
Keyword(s):  
Power System ◽  
Fundamental Frequency ◽  
Frequency Measurement ◽  
Least Square ◽  
Signal Model ◽  
Least Square Estimation ◽  
Signal Process ◽  
Frequency Detection ◽  
Novel Method ◽  
Subspace Estimation

A novel method on frequency measurement based on improved TLS-ESPRIT (total least square estimation of signal parameters via rotational invariance techniques) is proposed in this paper with the research on fundamental frequency measurement in power system. TLS-ESPRIT is belong to subspace estimation in modern signal process. Noise is included in signal model, so it is independent on noise. But the same multi-poles cannot be taken when signal is in noise and based on TLS-ESPRIT. Multiple poles restoring is presented to take the true poles accurately. It is revealed that fundamental frequency is detected accurately in harmonics, interharmonics, noise and frequency fluctuations and better anti-noise ability in particular better adaptiveness on time varying signal in amplitude by simulation results.

Analytical and experimental analysis of axial force generated by a drive shaft system

2020 ◽  
Vol 234 (4) ◽  
pp. 691-706 ◽  
Author(s):  
Huayuan Feng ◽  
Subhash Rakheja ◽  
Wen-Bin Shangguan
Keyword(s):  
Dynamic Model ◽  
Axial Force ◽  
Friction Model ◽  
Drive Shaft ◽  
Contact Model ◽  
Model Parameters ◽  
Shaft System ◽  
Multibody Dynamic ◽  
Generated Axial Force ◽  
Operational Factors

The drive shaft system with a tripod joint is known to cause lateral vibration in a vehicle due to the axial force generated by various contact pairs of the tripod joint. The magnitude of the generated axial force, however, is related to various operating factors of the drive shaft system in a complex manner. The generated axial force due to a drive shaft system with a tripod joint and a ball joint was experimentally characterized considering ranges of operational factors, namely, the input toque, the shaft rotational speed, the articulation angle, and the friction. The data were analyzed to establish an understanding of the operational factors on the generated axial force. Owing to the observed significant effects of all the factors, a multibody dynamic model of the drive shaft system was formulated for predicting generated axial force under different operating conditions. The model integrated the roller–track contact model and the velocity-based friction model. Based on a quasi-static finite element model, a new methodology was proposed for identifying the roller–track contact model parameters, namely, the contact stiffness and force index. To further enhance the calculation accuracy of the multibody dynamic model, a new methodology for identifying the friction model parameters and the force index was proposed by using the measured data. The validity of the model was demonstrated by comparing the model-predicted and measured magnitudes of generated axial force for the ranges of operating factors considered. The results showed that the generated axial force of the drive shaft system can be calculated more accurately and effectively by using the identified friction and contact parameters in the paper.

scholarly journals New Lindley Half Cauchy Distribution: Theory and Applications

2020 ◽  
Vol 9 (4) ◽  
pp. 1-7
Keyword(s):  
Maximum Likelihood ◽  
Real Data ◽  
Cauchy Distribution ◽  
Least Square ◽  
Cumulative Distribution ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Model Parameters ◽  
Data Set ◽  
Von Mises

In this paper, we have defined a new two-parameter new Lindley half Cauchy (NLHC) distribution using Lindley-G family of distribution which accommodates increasing, decreasing and a variety of monotone failure rates. The statistical properties of the proposed distribution such as probability density function, cumulative distribution function, quantile, the measure of skewness and kurtosis are presented. We have briefly described the three well-known estimation methods namely maximum likelihood estimators (MLE), least-square (LSE) and Cramer-Von-Mises (CVM) methods. All the computations are performed in R software. By using the maximum likelihood method, we have constructed the asymptotic confidence interval for the model parameters. We verify empirically the potentiality of the new distribution in modeling a real data set.

Sign in / Sign up

Export Citation Format

Share Document