Research on Error Modeling and Compensation Method of Hot Rolling Shape Setting Model Based on Cluster and Neural Network

The position and orientation system (POS) is a key equipment for airborne remote sensing systems, which provides high-precision position, velocity, and attitude information for various imaging payloads. Temperature error is the main source that affects the precision of POS. Traditional temperature error model is single temperature parameter linear function, which is not sufficient for the higher accuracy requirement of POS. The traditional compensation method based on neural network faces great problem in the repeatability error under different temperature conditions. In order to improve the precision and generalization ability of the temperature error compensation for POS, a nonlinear multiparameters temperature error modeling and compensation method based on Bayesian regularization neural network was proposed. The temperature error of POS was analyzed and a nonlinear multiparameters model was established. Bayesian regularization method was used as the evaluation criterion, which further optimized the coefficients of the temperature error. The experimental results show that the proposed method can improve temperature environmental adaptability and precision. The developed POS had been successfully applied in airborne TSMFTIS remote sensing system for the first time, which improved the accuracy of the reconstructed spectrum by 47.99%.

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Nonlinear Compensation Method for Transducer Based on Neural Network with Fourier Functions

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.220-223.1843 ◽

2012 ◽

Vol 220-223 ◽

pp. 1843-1847

Author(s):

Nan Lan Wang ◽

Ming Shan Cai

Keyword(s):

Neural Network ◽

Fourier Series ◽

Compensation Method ◽

Measured Temperature ◽

Temperature Transducer ◽

Model Based ◽

Basic Functions ◽

Thermistor Temperature ◽

Nonlinear Compensation

Aiming to solve the problems in the non-linearity of thermistor temperature transducer, a compensate model based on neural network (NN) is proposed. The basic idea is using Fourier series as the basic functions of NN，the output of transducer as input samples of NN and the temperature as the expectation output of NN. The output of NN is used to approximate to the measured temperature by adjusting the weights. The results show the proposed method is effective in raising accuracy.

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Study of error compensations and sensitivity analysis for 6-Dof serial robot

Engineering Computations ◽

10.1108/ec-03-2020-0128 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Xi Luo ◽

Yingjie Zhang ◽

Lin Zhang

Keyword(s):

Sensitivity Analysis ◽

Error Compensation ◽

Error Model ◽

Error Modeling ◽

Compensation Method ◽

Joint Clearance ◽

Positioning Accuracy ◽

Content Type ◽

Model Based ◽

Serial Robot

Purpose The purpose of this paper is to improve the positioning accuracy of 6-Dof serial robot by the way of error compensation and sensitivity analysis. Design/methodology/approach In this paper, the Denavit–Hartenberg matrix is used to construct the kinematics models of the robot; the effects from individual joint and several joints on the end effector are estimated by simulation. Then, an error model based on joint clearance is proposed so that the positioning accuracy at any position of joints can be predicted for compensation. Through the simulation of the curve path, the validity of the error compensation model is verified. Finally, the experimental results show that the error compensation method can improve the positioning accuracy of a two joint exoskeleton robot by nearly 76.46%. Findings Through the analysis of joint error sensitivity, it is found that the first three joints, especially joint 2, contribute a lot to the positioning accuracy of the robot, which provides guidance for the accuracy allocation of the robot. In addition, this paper creatively puts forward the error model based on joint clearance, and the error compensation method which decouples the positioning accuracy into joint errors. Originality/value It provides a new idea for error modeling and error compensation of 6-Dof serial robot. Combining sensitivity analysis results with error compensation can effectively improve the positioning accuracy of the robot, and provide convenience for welding robot and other robots that need high positioning accuracy.

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