基于小波网络的发动机瞬态工况进气流量动态辨识与预测研究

由于发动机进气系统具有复杂的非线性动态特性,因此构建了进气流量小波网络辨识与预测模型,并利用最小二乘法(DLS)对小波网络参数和预测控制率进行了学习和优化,以提高小波网络预测模型的可靠性和预测精度。作为对比建立了基于BP神经网络的预测模型,并利用瞬态工况试验数据分别对两种模型进行了仿真研究。结果表明,小波网络模型能有效地预测发动机瞬态工况进气流量,与BP神经网络预测模型相比,误差精度更高,可用于发动机瞬态工况空燃比的精确控制。

Dynamic Recognition and Prediction of Intake Air Flow Ratio under Engine Instantaneous Condition Based on Wavelet Networks

The recognition and prediction of intake air flow was built based on wavelet networks due to the non-linear and dynamic property of engine intake system.To improve the reliability and precision of wavelet network model,the parameters and control law were learned and optimized with Davidon least square (DLS) algorithm.Then BP neural network model for intake air flow under transient conditions was established and compared with wavelet network model based on the actual acquisition data.The results show that the wavelet network model can successfully forecast intake air flow of gasoline engine under transient conditions and is superior to BP neural network model due to higher accuracy.Accordingly,the model may apply to the accurate control of transient air fuel ratio.