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Model Predictive Control of Robotic Grinding Based on Deep Belief Network

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  • Shouyan Chen
  • Tie Zhang
  • Yanbiao Zou
  • Meng Xiao

Abstract

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.

Suggested Citation

  • Shouyan Chen & Tie Zhang & Yanbiao Zou & Meng Xiao, 2019. "Model Predictive Control of Robotic Grinding Based on Deep Belief Network," Complexity, Hindawi, vol. 2019, pages 1-12, March.
    • Handle: RePEc:hin:complx:1891365
    DOI: 10.1155/2019/1891365
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    References listed on IDEAS

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    1. Rong Mei & ChengJiang Yu, 2017. "Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation," Complexity, Hindawi, vol. 2017, pages 1-12, August.
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