{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T14:53:46Z","timestamp":1763564026252,"version":"3.41.2"},"reference-count":20,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2019,3,27]],"date-time":"2019-03-27T00:00:00Z","timestamp":1553644800000},"content-version":"vor","delay-in-days":85,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2019,1]]},"abstract":"<jats:p>Considering the influence of rigid\u2010flexible 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\u2010flexible 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\u2010forward 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\u2010forward and feedback based combination control, reducing control overflow and system oscillation caused by inaccurate feedback control.<\/jats:p>","DOI":"10.1155\/2019\/1891365","type":"journal-article","created":{"date-parts":[[2019,3,27]],"date-time":"2019-03-27T23:31:14Z","timestamp":1553729474000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Model Predictive Control of Robotic Grinding Based on Deep Belief Network"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3052-3625","authenticated-orcid":false,"given":"Shouyan","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9716-3970","authenticated-orcid":false,"given":"Tie","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yanbiao","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Meng","family":"Xiao","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2019,3,27]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2017.09.004"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-016-9461-z"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2017.11.004"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/7413642"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.07.047"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/70.864223"},{"key":"e_1_2_9_7_2","first-page":"1","article-title":"Recurrent fuzzy wavelet neural networks based on robust adaptive sliding mode control for industrial robot manipulators","volume":"18","author":"Yen V. 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