Model Predictive Control of Robotic Grinding Based on Deep Belief Network
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/1891365 |
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| _version_ | 1832565756569583616 |
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| author | Shouyan Chen Tie Zhang Yanbiao Zou Meng Xiao |
| author_facet | Shouyan Chen Tie Zhang Yanbiao Zou Meng Xiao |
| author_sort | Shouyan Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-efa07b3a1dd040688575d861e1b30f24 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-efa07b3a1dd040688575d861e1b30f242025-02-03T01:06:55ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/18913651891365Model Predictive Control of Robotic Grinding Based on Deep Belief NetworkShouyan Chen0Tie Zhang1Yanbiao Zou2Meng Xiao3Guangzhou University, ChinaSouth China University of Technology, ChinaSouth China University of Technology, ChinaSouth China University of Technology, ChinaConsidering 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.http://dx.doi.org/10.1155/2019/1891365 |
| spellingShingle | Shouyan Chen Tie Zhang Yanbiao Zou Meng Xiao Model Predictive Control of Robotic Grinding Based on Deep Belief Network Complexity |
| title | Model Predictive Control of Robotic Grinding Based on Deep Belief Network |
| title_full | Model Predictive Control of Robotic Grinding Based on Deep Belief Network |
| title_fullStr | Model Predictive Control of Robotic Grinding Based on Deep Belief Network |
| title_full_unstemmed | Model Predictive Control of Robotic Grinding Based on Deep Belief Network |
| title_short | Model Predictive Control of Robotic Grinding Based on Deep Belief Network |
| title_sort | model predictive control of robotic grinding based on deep belief network |
| url | http://dx.doi.org/10.1155/2019/1891365 |
| work_keys_str_mv | AT shouyanchen modelpredictivecontrolofroboticgrindingbasedondeepbeliefnetwork AT tiezhang modelpredictivecontrolofroboticgrindingbasedondeepbeliefnetwork AT yanbiaozou modelpredictivecontrolofroboticgrindingbasedondeepbeliefnetwork AT mengxiao modelpredictivecontrolofroboticgrindingbasedondeepbeliefnetwork |