CAJ | 학술논문

采用神经网络实现在复杂工况下柴油共轨压力调节器PID控制系统的参数整定，建立了YN4100QB柴油机共轨压力调节器AMESim仿真模型，利用MATLAB/Simulink设计了柴油机共轨压力调节器控制系统BP神经网络PID仿真模块，在AMESim环境下对共轨压力调节器进行了动态仿真。仿真试验表明，神经网络PID制器具有良好的动态性能和较强的鲁棒性，与传统PID控制相比，大大改善了共轨压力调节器在复杂工况下压力调节的性能。
채용신경망락실현재복잡공황하시유공궤압력조절기PID공제계통적삼수정정，건립료YN4100QB시유궤공궤압력조절기AMESim방진모형，이용MATLAB/Simulink설계료시유궤공궤압력조절기공제계통BP신경망락PID방진모괴，재AMESim배경하대공궤압력조절기진행료동태방진。방진시험표명，신경망락PID제기구유량호적동태성능화교강적로봉성，여전통PID공제상비，대대개선료공궤압력조절기재복잡공황하압력조절적성능。
In this paper, the parameter of PID controller of diesel engine common rail pressure regulator in complex conditions is set by neural networks. AMESim simulation model of YN4100QB diesel common rail pressure regulator is Established, BP neural network PID simulation module of diesel engine common rail pressure regulator control system is designed by MATLAB/Simulink and simulated dynamically in AMESim. The simulation experiment shows that compared with conventional PID control, the neural network PID controller has good dynamic performance and robustness and greatly improved the regulated performance of the rail pressure regulator pressure regulator in complex conditions.