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.
Highlights
The deformation occurs during robotic grinding process has significant impact on robotic grinding dynamic and robotic grinding performance [1, 2]
For the model training of robotic grinding status at time ti+0, the deep belief network (DBN) realizes fitting at 31st epoch and BP network realizes fitting at 52nd epoch
This is because that the model predictive control approach can predict the future grinding deviation based on acquired information and result in a control compensation to reduce the coming up force deviation and feed rate fluctuation
Summary
The deformation occurs during robotic grinding process has significant impact on robotic grinding dynamic and robotic grinding performance [1, 2]. The real-time force control approaches presented in current studies include adaptive control, fuzzy control, and control based on neural network [4]. Yen [7] proposed an adaptive control method based on recursive fuzzy wavelet neural network to optimize motion control parameters of three-axis robot in real time. Dalamagkidis [16] proposed a nonlinear model predictive control approach based on recurrent neural network to achieve the predictive control of propeller selfrotation process while unmanned aerial vehicle engine is damaged. A model predictive control approach based on a deep belief network (DBN) is proposed to control robotic deformation and reduce rigid-flexible effect on robotic grinding dynamics. Since the accurate parameters of robotic grinding dynamics model and model predictive controller are hard to acquire, a deep belief network is designed to access nonlinear predictive model of robotic grinding. Simulation and experiments are carried out to verify performance of the proposed approach
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