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Guo Jianxin1; Zhang Qiang2; Gao Xiaoshan1
Source Publicationjournalofsystemsscienceandcomplexity
AbstractIn this paper, a method of reducing the tracking error in CNC machining is proposed. The structured neural network is used to approximate the discontinuous friction in CNC machining, which has jump points and uncertainties. With the estimated nonlinear friction function, the reshaped trajectory can be computed from the desired one by solving a second order ODE such that when the reshaped trajectory is fed into the CNC controller, the output is the desired trajectory and the tracking error is eliminated in certain sense. The proposed reshape method is also shown to be robust with respect to certain parameters of the dynamic system.
Funding Project[National Key Basic Research Project of China] ; [USA NSF] ; [Foundation of UPC for the Author of National Excellent Doctoral Dissertation]
Document Type期刊论文
Recommended Citation
GB/T 7714
Guo Jianxin,Zhang Qiang,Gao Xiaoshan. trackingerrorreductionincncmachiningbyreshapingthekinematictrajectory[J]. journalofsystemsscienceandcomplexity,2013,26(5):817.
APA Guo Jianxin,Zhang Qiang,&Gao Xiaoshan.(2013).trackingerrorreductionincncmachiningbyreshapingthekinematictrajectory.journalofsystemsscienceandcomplexity,26(5),817.
MLA Guo Jianxin,et al."trackingerrorreductionincncmachiningbyreshapingthekinematictrajectory".journalofsystemsscienceandcomplexity 26.5(2013):817.
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