涡轮增压汽油机气路预测模型的建立与预测控制（英文）
发布时间：2018-07-27 13:18
【摘要】：针对涡轮增压汽油机气路系统中节气门与废气旁通阀动力学耦合、机理建模复杂的问题,本文提出基于神经网络模型的气路系统预测控制方法,实现了节气门与废气旁通阀的协调控制.首先,针对涡轮增压汽油机气路系统map与机理混合描述的特性,利用系统的输入输出数据,采用反向传播神经网络(back propagation neural network,BPNN)训练得到一个非线性气路模型;其次,基于泰勒展开式对预测模型进行线性化,并对模型的精度进行了验证,进而利用该模型预测系统的未来动态;然后,在考虑系统存在输入约束的条件下,设计了一个线性模型预测控制器对节气门与废气旁通阀进行协调控制,实现了进气歧管压力和升压的跟踪控制进而满足发动机的扭矩需求;最后,通过离线仿真和基于d SPACE的快速原型实验(rapid control prototyping,RCP)验证了控制系统的有效性和实时性.
[Abstract]:Aiming at the problem of dynamic coupling of throttle and exhaust gas bypass valve in turbocharged gasoline engine gas path system and complicated mechanism modeling, a predictive control method of gas path system based on neural network model is presented in this paper. The coordinated control of throttle and exhaust gas bypass valve is realized. Firstly, a nonlinear gas path model is obtained by using the input and output data of a turbocharged gasoline engine based on the hybrid description of map and mechanism. A nonlinear gas path model is obtained by using the backpropagation neural network (back propagation neural network BPNN). The prediction model is linearized based on Taylor expansion, and the accuracy of the model is verified, and then the future dynamics of the system are predicted by using the model. A linear model predictive controller is designed for coordinated control of throttle and exhaust gas bypass valve. The tracking control of intake manifold pressure and boost pressure is realized to meet the torque requirement of engine. The effectiveness and real-time performance of the control system are verified by off-line simulation and (rapid control prototyping experiment based on d SPACE.
【作者单位】： 吉林大学汽车仿真与控制重点实验室;吉林大学通信工程学院;中国一汽集团公司研究设计中心;
【基金】：Supported by National Natural Science Foundation of China(61703177,61520106008) Jilin Provincial Science and Technology Department Project(20170520067JH) Jilin Provincial Education Department Project(JJKH20170801KJ)
【分类号】：TK411;TP183
，
本文编号：2147964
[Abstract]:Aiming at the problem of dynamic coupling of throttle and exhaust gas bypass valve in turbocharged gasoline engine gas path system and complicated mechanism modeling, a predictive control method of gas path system based on neural network model is presented in this paper. The coordinated control of throttle and exhaust gas bypass valve is realized. Firstly, a nonlinear gas path model is obtained by using the input and output data of a turbocharged gasoline engine based on the hybrid description of map and mechanism. A nonlinear gas path model is obtained by using the backpropagation neural network (back propagation neural network BPNN). The prediction model is linearized based on Taylor expansion, and the accuracy of the model is verified, and then the future dynamics of the system are predicted by using the model. A linear model predictive controller is designed for coordinated control of throttle and exhaust gas bypass valve. The tracking control of intake manifold pressure and boost pressure is realized to meet the torque requirement of engine. The effectiveness and real-time performance of the control system are verified by off-line simulation and (rapid control prototyping experiment based on d SPACE.
【作者单位】： 吉林大学汽车仿真与控制重点实验室;吉林大学通信工程学院;中国一汽集团公司研究设计中心;
【基金】：Supported by National Natural Science Foundation of China(61703177,61520106008) Jilin Provincial Science and Technology Department Project(20170520067JH) Jilin Provincial Education Department Project(JJKH20170801KJ)
【分类号】：TK411;TP183
，
本文编号：2147964
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