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Vol. 121, Issue 10, October 2010, pp.18-28

 

Bullet

Improved RBF Neural Network Based Soft Sensor: Application to the Optimal Robust Calibration

of a Six Degrees of Freedom Parallel Kinematics Manipulator

 

Dan ZHANG and Zhen GAO

Faculty of Engineering and Applied Science,

University of Ontario Institute of Technology, Oshawa, Ontario, L1H 7K4 Canada

Tel.: 1-905-721-8668 ext. 3504, fax: 1-905-721-3370,

E-mail: Dan.Zhang@uoit.ca, Zhen.Gao@uoit.ca

 

 

Received: 25 September 2010   /Accepted: 18 October 2010   /Published: 26 October 2010

 

Abstract: Accuracy is paramount for the further development of parallel mechanism in real world, especially in industry. Previous research was focused on the improvement of rigidity and load capacity which is related with the stiffness matrix of closed loop kinematic structure. However, if the mechanical structure has been predefined or fabricated, stiffness matrix only can search for the optimal configuration in the workspace, but fails to enhance the accuracy at a given pose. In this research, the concept of optimal robust calibration is developed as an effective approach to largely reduce various errors of the predefined parallel mechanism. A novel coevolutionary radial basis function (RBF) neural network based soft sensor is proposed to implement the optimal robust calibration procedure. A six- degrees-of-freedom parallel kinematics manipulator is selected as the case study to verify the proposed methodology. The results demonstrate that the coevolutionary neural network possesses the excellent performance as a smart soft sensor for the calibration of closed loop kinematic structure.

 

Keywords: : Coevolutionary RBF neural network, Soft sensor, Optimal robust calibration, Black box, Parallel kinematics manipulator

 

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