摘要
利用神经网络训练数据建立了发动机数学模型。针对目前起步控制策略大多没有自学习功能的现状,基于补偿模糊神经网络,以油门开度及其变化率为输入变量,提出了一种汽车双离合器式自动变速器起步控制策略。采用起步时间、滑摩功、冲击度、发动机最高转速和同步转速等指标检验仿真结果。结果表明,基于补偿模糊神经网络的起步控制策略在各性能指标方面均优于原控制策略,并具有较强的自适应能力。
An engine mathematic model is established Wlttl neural network training oata. n start coI^trol ~ti^ttv^y ~u~ dual clutch transmission is put forward with throttle opening and it change rate as input variable based on compensation fuzzy neural network according to the current status that no self-learning function is available for most start control strategies. Simulation results are examined through some indexes such as Start time, slip energy, impact, engine maximum speed and synchronous speed. Simulation results suggest that the control strategy presented is better than the original one, and it is of great self-adaptive ability.
出处
《汽车技术》
北大核心
2011年第5期5-10,共6页
Automobile Technology
基金
上海市科委重大攻关项目"双离合器式自动变速器电子控制单元及硬件在环仿真试验台的研制"(06dz11002)和"轿车用干式双离合器自动变速器控制系统开发"(08dz1150401)
关键词
自动变速器
双离合器式
补偿模糊神经网络
起步控制
Automatic transmission, Dual clutch type, Compensation fuzzy neural network,Start control
