Model Predictive Control of Robotic Grinding Based on Deep Belief Network

• Published in: Complexity (New York, N.Y.) Vol. 2019; no. 2019; pp. 1 - 12
• Main Authors: Xiao, Meng, Zou, Yanbiao, Zhang, Tie, Chen, Shouyan
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2019; Hindawi; John Wiley & Sons, Inc; Hindawi Limited; Hindawi-Wiley
• Subjects: Algorithms; Analysis; Belief networks; Computer simulation; Control systems; Control theory; Costs (Law); Deformation mechanisms; Feedback control; Feedforward control; Grinding; Grinding tools; Mathematical models; Motion control; Network management systems; Neural networks; Optimization; Parameters; Predictive control; Robot control; Robotics; Robots
• ISSN: 1076-2787; 1099-0526
• DOI: 10.1155/2019/1891365

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.