The transfer function of the wave compensation system is deduced. The interference signal acting on the system is eliminated by the feed-forward compensation correction method. The pole assignment of the system is carried out after eliminating the interference signal. The genetic PID algorithm is proposed, and the genetic PID controller is designed. The control block diagram of the wave compensation system based on the genetic PID control algorithm is established, and the optimal index and PID parameters are optimized by the crossover and mutation operators of the genetic particle swarm optimization algorithm. The simulations and experiments of the system show that the control performance of the wave compensation system based on the genetic PID algorithm is greatly improved. The higher control precision is obtained. The anti-interference ability and the robustness of the system are increased. The accuracy of the control method is verified.
The control of interfacial thermal conductivity is the key to two−dimensional heterojunction in semiconductor devices. In this paper, by using non−equilibrium molecular dynamics (NEMD) simulations, we analyze the regulation of interfacial thermal energy transport in graphene (Gr)/hexagonal boron nitride (h-BN) heterojunctions and reveal the variation mechanism of interfacial thermal energy transport. The calculated results show that 2.16% atomic doping can effectively improve interfacial heat transport by more than 15.6%, which is attributed to the enhanced phonon coupling in the mid−frequency region (15–25 THz). The single vacancy in both N and B atoms can significantly reduce the interfacial thermal conductivity (ITC), and the ITC decreases linearly with the increase in vacancy defect concentration, mainly due to the single vacancy defects leading to an increased phonon participation rate (PPR) below 0.4 in the low-frequency region (0–13 THz), which shows the phonon the localization feature, which hinders the interfacial heat transport. Finally, a BP neural network algorithm is constructed using machine learning to achieve fast prediction of the ITC of Gr/h-BN two-dimensional heterogeneous structures, and the results show that the prediction error of the model is less than 2%, and the method will provide guidance and reference for the design and optimization of the ITC of more complex defect-state heterogeneous structures.
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