A Compensation Control Method Using Neural Network for Mechanical Deflection Error in SCARA Robot with Random PayloadCompensation Control Method Using Neural Network for Mechanical Deflection Error in
(주)코리아스칼라
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- 2016.04.01
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- 2011.09
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서지정보
ㆍ발행기관 : 한국기계기술학회
ㆍ수록지정보 : 한국기계기술학회지 / 13권 / 3호
ㆍ저자명 : Jong Shin, Lee
목차
Abstract
1. Introduction
2. MODELING AND ANALYSIS
2.1 Modeling and Geometrical Parameters
2.2 Extraction of Analytical Data
3. Neural Network Learning
3.1 Structure of Neural Network
3.2. Neural Network Learning
4. Simulation Using Computer
4.1 Deflection According to Changes in Robot’s Posture and Payload part
4.2 Simulation for Random Path
4.3 Results Analysis
5. Conclusions
References
영어 초록
This study proposes the compensation method for the mechanical deflection error of a SCARA robot. While most studies on the related subject have dealt with the development of a control algorithm for improvement of robot accuracy, this study presents the control method reflecting the mechanical deflection error which is predicted in advance. The deflection at the end of the gripper of SCARA robot is caused by the self-weights and payloads of Arm 1, Arm 2 and quill. If the deflection is constant even though robot’ posture and payload vary, there may not be a big problem on robot accuracy because repetitive accuracy, that is relative accuracy, is more important than absolute accuracy in robot. The deflection in the end of the gripper varies as robot’ posture and payload change. That’ why the moments , and working on every joint of a robot vary with robot’ posture and payload size. This study suggests the compensation method which predicts the deflection in advance with the variations in robot’ posture and payload using neural network. To do this, I chose the posture of robot and the payloads at random, found the deflections by the FEM analysis, and then on the basis of this data, made compensation possible by predicting deflections in advance successively with the variations in robot’ posture and payload through neural network learning.
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