scholarly journals Structural Parameter Identification of Articulated Arm Coordinate Measuring Machines

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Guanbin Gao ◽  
Huaishan Zhang ◽  
Xing Wu ◽  
Yu Guo
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Structural Parameters ◽  
Kinematic Model ◽  
Structural Parameter ◽  
Identification Algorithm ◽  
Data Set ◽  
Identification Approach ◽  
Measuring Machine ◽  
Identification Model

Precise structural parameter identification of a robotic articulated arm coordinate measuring machine (AACMM) is essential for improving its measuring accuracy, particularly in robotic applications. This paper presents a constructive parameter identification approach for robotic AACMMs. We first develop a mathematical kinematic model of the AACMM based on the Denavit-Hartenberg (DH) approach established for robotic systems. This model is further calibrated and verified via the practical test data. Based on the difference between the calculated coordinates of the AACMM probe via the kinematic model and the given reference coordinates, a parameter identification approach is proposed to estimate the structural parameters in terms of the test data set. The Jacobian matrix is further analyzed to determine the solvability of the identification model. It shows that there are two coupling parameters, which can be removed in the regressor. Finally, a parameter identification algorithm taking the least-square solution of the identification model as the structural parameters by using the obtained poses data is suggested. Practical experiments based on a robotic AACMM test rig are carried out, and the results reveal the effectiveness and robustness of the proposed identification approach.

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.

A novel approach for experimental identification of vehicle dynamic parameters

2020 ◽  
Vol 234 (10-11) ◽  
pp. 2634-2648
Author(s):  
Di Yao ◽  
Philipp Ulbricht ◽  
Stefan Tonutti ◽  
Kay Büttner ◽  
Prokop Günther
Keyword(s):  
Parameter Identification ◽  
Dynamic Modeling ◽  
Trajectory Optimization ◽  
Dynamic Parameters ◽  
Vehicle Dynamic ◽  
Test Rig ◽  
Vehicle Simulation ◽  
Virtual Test ◽  
Identification Approach ◽  
Measuring Machine

Pervasive applications of the vehicle simulation technology are a powerful motivation for the development of modern automobile industry. As basic parameters of road vehicle, vehicle dynamic parameters can significantly influence the ride comfort and dynamics of vehicle, and therefore have to be calculated accurately to obtain reliable vehicle simulation results. Aiming to develop a general solution, which is applicable to diverse test rigs with different mechanisms, a novel model-based parameter identification approach using optimized excitation trajectory is proposed in this paper to identify the vehicle dynamic parameters precisely and efficiently. The proposed approach is first verified against a virtual test rig using a universal mechanism. The simulation verification consists of four sections: (a) kinematic analysis, including the analysis of forward/inverse kinematic and singularity architecture; (b) dynamic modeling, in which three kinds of dynamic modeling method are used to derive the dynamic models for parameter identification; (c) trajectory optimization, which aims to search for the optimal trajectory to minimize the sensitivity of parameter identification to measurement noise; and (d) multibody simulation, by which vehicle dynamic parameters are identified based on the virtual test rig in the simulation environment. In addition to the simulation verification, the proposed parameter identification approach is applied to the real test rig (vehicle inertia measuring machine) in laboratory subsequently. Despite the mechanism difference between the virtual test rig and vehicle inertia measuring machine, this approach has shown an excellent portability. The experimental results indicate that the proposed parameter identification approach can effectively identify the vehicle dynamic parameters without a high requirement of movement accuracy.

Kinematic Modeling and Error Analysis for a 3DOF Parallel-Link Coordinate Measuring Machine

2006 ◽  
Vol 532-533 ◽  
pp. 313-316 ◽  
Author(s):  
De Jun Liu ◽  
Hua Qing Liang ◽  
Hong Dong Yin ◽  
Bu Ren Qian
Keyword(s):  
Kinematic Model ◽  
Coordinate Measuring Machine ◽  
Inverse Kinematic ◽  
Differential Method ◽  
Error Model ◽  
Structural Parameter ◽  
Kinematic Modeling ◽  
Position Errors ◽  
Measuring Machine ◽  
Parallel Link

First, the forward kinematic model, the inverse kinematic model and the error model of a kind of coordinate measuring machine (CMM) using 3-DOF parallel-link mechanism are established based on the spatial mechanics theory and the total differential method, and the error model is verified by computer simulation. Then, the influence of structural parameter errors on probe position errors is systematically considered. This research provides an essential theoretical basis for increasing the measuring accuracy of the parallel-link coordinate measuring machine. It is of particular importance to develop the prototype of the new measuring equipment.

scholarly journals Improved parameter identification algorithm for ship model based on nonlinear innovation decorated by sigmoid function

2021 ◽  
Author(s):  
Xianku Zhang ◽  
Baigang Zhao ◽  
Guoqing Zhang
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
Test Data ◽  
Stochastic Gradient ◽  
Gradient Algorithm ◽  
Identification Algorithm ◽  
Sigmoid Function ◽  
Stochastic Gradient Algorithm ◽  
Identification Test ◽  
Small Test

Abstract This paper investigates the problem of parameter identification for ship nonlinear Nomoto model with small test data, a nonlinear innovation-based identification algorithm is presented by embedding sigmoid function in the stochastic gradient algorithm. To demonstrate the validity of the algorithm, an identification test is carried out on the ship ‘SWAN’ with only 26 sets of test data. Furthermore, the identification effects of the least squares algorithm, original stochastic gradient algorithm and the improved stochastic gradient algorithm based on nonlinear innovation are compared. Generally, the stochastic gradient algorithm is not suitable for the condition of small test data. The simulation results indicate that the improved stochastic gradient algorithm with sigmoid function greatly increases its accuracy of parameter identification and has 14.2% up compared with the least squares algorithm. Then the effectiveness of the algorithm is verified by another identification test on the ship ‘Galaxy’, the accuracy of parameter identification can reach more than 95% which can be used in ship motion simulation and controller design. The proposed algorithm has advantages of the small test data, fast speed and high accuracy of identification, which can be extended to other parameter identification systems with less sample data.

scholarly journals Structural Parameter Identification of Fixed End Beams by Inverse Method Using Measured Natural Frequencies

2008 ◽  
Vol 15 (5) ◽  
pp. 505-515 ◽  
Author(s):  
T. Türker ◽  
A. Bayraktar
Keyword(s):  
Differential Equation ◽  
Parameter Identification ◽  
Inverse Method ◽  
Natural Frequencies ◽  
Structural Parameters ◽  
Added Mass ◽  
Modal Testing ◽  
Dynamic Responses ◽  
Structural Parameter ◽  
Fixed End

Structural parameter identification based on the measured dynamic responses has become very popular recently. This paper presents structural parameter identification of fixed end beams by inverse method using measured natural frequencies. An added mass is used as a modification tool. The measurements of the flexural vibrations of a fixed end beam with and without added mass are performed by using experimental modal testing. The solution of free bending transverse vibration of the beam is obtained by solving the differential equation motion of Bernoulli-Euler beam. By introducing the natural frequencies from experimental measurements into the solution of differential equation, the structural parameters of the fixed end beam are calculated.It is seen from the results that the values of the mass distribution and elasticity modulus identified using the first natural frequency of the beam nearly close to the real values. Besides, the theoretical frequencies obtained using the identified structural parameters also close to the measured frequencies.

STRUCTURAL PARAMETER IDENTIFICATION USING DAMPED TRANSFER MATRIX AND STATE VECTORS

2013 ◽  
Vol 13 (04) ◽  
pp. 1250076 ◽  
Author(s):  
P. NANDAKUMAR ◽  
K. SHANKAR
Keyword(s):  
Transfer Matrix ◽  
Optimization Algorithm ◽  
State Vector ◽  
Degrees Of Freedom ◽  
Structural Parameters ◽  
Frame Structure ◽  
Structural Parameter ◽  
Identification Algorithm ◽  
Lumped Mass ◽  
Mass System

A new method for identification of structural parameters is proposed using Damped Transfer Matrices (DTM) and state vectors. A new transfer matrix is derived for continuous mass systems including the damping parameters. The state vector at a location is the sum of the internal and external contributions of displacements, forces, and moments at that point, when it is multiplied with the transfer matrix, state vector at the adjacent location is obtained. The structural identification algorithm proposed here involves prediction of displacement responses at selected locations of the structure using DTM and compares them with the measured responses at the respective locations. The mean square deviations between the measured and predicted responses at all locations are minimized using a nonclassical optimization algorithm, and the optimization variables are the unknown stiffness and damping parameters in the DTM. A nonclassical heuristic Particle Swarm Optimization algorithm (PSO) is used, since it is especially suited for global search. This DTM algorithm with successive identification strategy is applied on one element or sub-structure of a structure at a time and identifies all the parameters of adjacent elements successively. The algorithm is applied on numerically simulated experiments of two structures such as ten degrees of freedom lumped mass system and a cantilever with seven finite elements and one sub-structure of a nine member frame structure. Also this algorithm is verified experimentally on a sub-structure of a fixed beam. The main advantage of this algorithm is that it can be used for the local identification in a zone in a structure without modeling the entire global structure.

Kinematic Modeling and Analysis of Articulated Arm Coordinate Measuring Machines

2012 ◽  
Vol 241-244 ◽  
pp. 494-497
Author(s):  
Guan Bin Gao ◽  
Wen Wang ◽  
Hong Qiang Li ◽  
Jian Jun Zhou
Keyword(s):  
Structural Characteristics ◽  
Structural Parameters ◽  
Kinematic Model ◽  
Coordinate Measuring Machine ◽  
Measurement Range ◽  
Kinematic Modeling ◽  
Modeling And Analysis ◽  
Graphical Simulation ◽  
Measuring Machine ◽  
Numerical Computing

The articulated arm coordinate measuring machine (AACMM) is a new type coordinate measuring machine (CMM) base on the linkage structure with the characteristics of small size, light weight, large measurement range and flexible movement. The kinematic modeling methods of six degree of freedom (6-DOF) AACMMs are studied in this paper. By analyzing the structural characteristics of AACMMs the kinematic model of a 6-DOF AACMM with DH method was established. From the kinematic model the coordinate systems and structural parameters of the AACMM are obtained. Then the homogeneous transformation matrixes from the probe to the base of the AACMM are derived. Finally, methods of numerical computing and graphical simulation are used in verifying the kinematic model. The kinematic model provides a basis for measurement, calibration and error compensation of the AACMM.

How to Assess Prognostic Models for Survival Data: A Case Study in Oncology

2003 ◽  
Vol 42 (05) ◽  
pp. 564-571 ◽  
Author(s):  
M. Schumacher ◽  
E. Graf ◽  
T. Gerds
Keyword(s):  
Test Data ◽  
Survival Data ◽  
Prediction Error ◽  
Classification Scheme ◽  
Neural Nets ◽  
Brier Score ◽  
Data Set ◽  
Independent Test ◽  
Artificial Neural

Summary Objectives: A lack of generally applicable tools for the assessment of predictions for survival data has to be recognized. Prediction error curves based on the Brier score that have been suggested as a sensible approach are illustrated by means of a case study. Methods: The concept of predictions made in terms of conditional survival probabilities given the patient’s covariates is introduced. Such predictions are derived from various statistical models for survival data including artificial neural networks. The idea of how the prediction error of a prognostic classification scheme can be followed over time is illustrated with the data of two studies on the prognosis of node positive breast cancer patients, one of them serving as an independent test data set. Results and Conclusions: The Brier score as a function of time is shown to be a valuable tool for assessing the predictive performance of prognostic classification schemes for survival data incorporating censored observations. Comparison with the prediction based on the pooled Kaplan Meier estimator yields a benchmark value for any classification scheme incorporating patient’s covariate measurements. The problem of an overoptimistic assessment of prediction error caused by data-driven modelling as it is, for example, done with artificial neural nets can be circumvented by an assessment in an independent test data set.

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