%0 Journal Article
%T 基于Hammerstein结构的电子节气门动态非线性建模<br>Dynamic nonlinear system modeling of electronic throttle body based on Hammerstein structure
%A 杨新宇
%A 张臻
%A 谭清远
%A 陈翔
%A 周克敏
%J 北京航空航天大学学报
%D 2018
%R 10.13700/j.bh.1001-5965.2018.0198
%X 摘要 为实现对配装于5.7 L汽油发动机的某型汽车电子节气门（ETB）系统的鲁棒控制，需先建立ETB系统的非线性逆模型以抵消动态迟滞非线性对系统控制性能的影响，针对该ETB系统非线性特性进行了研究，基于Hammerstein模型结构对ETB的动态迟滞非线性进行了建模。首先为了描述ETB特殊的迟滞非线性特性，构造了一种新的静态迟滞算子作为Hammerstein系统中的非线性子系统并推导得到了静态迟滞算子的解析逆；然后基于迟滞逆补偿策略估计出Hammerstein系统中的中间不可测变量；最后基于最小二乘估计法辨识得到Hammerstein系统中的线性子系统。建模结果与实验结果对比表明本文模型能够很好地描述ETB的动态迟滞特性。<br>Abstract：In order to realize the robust control of an electronic throttle body (ETB) system equipped with 5.7 L gasoline engine, the nonlinear inverse model of the ETB system must be established to counteract the effect of dynamic hysteresis nonlinearity on the control performance of the system. In this paper, the dynamic nonlinear characteristics of the ETB system are studied and a dynamic hysteresis model for the ETB system is proposed and identified based on the structure of Hammerstein system. It is challenging for the existing static hysteresis operators to cover the nonlinear characteristics of the ETB system. Thus, to describe the special hysteresis nonlinear characteristics of ETB system, a new static hysteresis nonlinear operator is constructed as the nonlinear subsystem for the Hammerstein model. The analytical inverse operator of the static hysteresis operator is also derived. The unmeasurable internal state in the Hammerstein system is then estimated based on the hysteresis inverse compensation strategy. Finally, the linear subsystem in the Hammerstein system is identified using the estimation method of least square. The comparison between the modeling results and experimental results shows that the proposed model can describe the dynamic hysteresis nonlinear characteristics of the ETB.
%K 电子节气门(ETB)
%K 迟滞非线性
%K Hammerstein模型
%K 迟滞逆补偿
%K 最小二乘估计&lt
%K br&gt
%K electronic throttle body (ETB)
%K hysteresis nonlinearity
%K Hammerstein model
%K hysteresis inverse compensation
%K least square estimation
%U http://bhxb.buaa.edu.cn/CN/abstract/abstract14671.shtml