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Model Predictive Control of Robotic Grinding Based on Deep Belief Network
Joint Authors
Chen, Shouyan
Zhang, Tie
Zou, Yanbiao
Xiao, Meng
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-03-27
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Considering the influence of rigid-flexible dynamics on robotic grinding process, a model predictive control approach based on deep belief network (DBN) is proposed to control robotic grinding deformation.
The rigid-flexible coupling dynamics of robotic grinding is first established, on the basis of which a robotic grinding prediction model is constructed to predict the change of robotic grinding status and perform feed-forward control.
A rolling optimization formula derived from the energy function is also established to optimize control output in real time and perform feedback control.
As the accurately model parameters are hard to obtain, a deep belief network is constructed to obtain the parameters of robotic grinding predictive model.
Simulation and experimental results indicate that the proposed model predictive control approach can predict abrupt change of robotic grinding status caused by deformation and perform a feed-forward and feedback based combination control, reducing control overflow and system oscillation caused by inaccurate feedback control.
American Psychological Association (APA)
Chen, Shouyan& Zhang, Tie& Zou, Yanbiao& Xiao, Meng. 2019. Model Predictive Control of Robotic Grinding Based on Deep Belief Network. Complexity،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1131136
Modern Language Association (MLA)
Chen, Shouyan…[et al.]. Model Predictive Control of Robotic Grinding Based on Deep Belief Network. Complexity No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1131136
American Medical Association (AMA)
Chen, Shouyan& Zhang, Tie& Zou, Yanbiao& Xiao, Meng. Model Predictive Control of Robotic Grinding Based on Deep Belief Network. Complexity. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1131136
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1131136