哈尔滨工程大学学报2018，Vol.39Issue(5)：908-914,7.DOI:10.11990/jheu.201704001
RBF神经网络理论在柴油机控制中的应用
An application of RBF neural network theory in diesel engine control
摘要
Abstract
In order to solve the problem that the speed governing effect of marine power station engine is affected by environment,this research studies the neural network controller on the speed control of the diesel engine by proposing a new radial-basis-function-proportional-integral-derivative(RBF-PID)speed control algorithm for the diesel engine based on RBF neural network theory.The proposed algorithm optimizes the control parameters of the speed loop con -troller in real time such that the initial weight effects on the RBF neural network are very small.In the case of muta-tion load,a series of simulation calculations are performed at different initial weights,and experimental verification is performed on a D6114 diesel generator.The simulation results match the experimental results.The results indicate that the impact of the initial parameters on The RBF-PID control algorithm is smaller than the impact of them on the BP-PID control algorithm,so the RBF-PID is more robust.The diesel engine adopted the RBF-PID control algorithm, and it can obtain a better speed regulation effect than traditional PID.At the same time,the influence of environmen-tal change on a diesel engine is smaller,and it does not take much time to train and learn the neural network;conse-quently,the proposed algorithm can meet the accuracy requirement of the two -stage power plant.关键词
柴油机/神经网络/转速/控制/电子调速器/BP神经网络/径向基/电站Key words
diesel engine/neural network/rotation speed/control/electronic governor/BP neural network/radial basis function/power station分类
能源科技引用本文复制引用
宋恩哲,王毓源,丁顺良,姚崇,刘剑刚,马修真..RBF神经网络理论在柴油机控制中的应用[J].哈尔滨工程大学学报,2018,39(5):908-914,7.基金项目
国家自然科学基金项目(51406040) （51406040）
工信部高技术项目(工信部联装[2013]412号). （工信部联装[2013]412号）
