Hajdarevic Bakir, Herrmann Jacob, Fonseca da Cruz Andrea, Kaczka David W
Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242 USA.
Department of Anesthesia, University of Iowa, Iowa City, IA 52242 USA.
Int J Model Identif Control. 2020;34(2):103-115. doi: 10.1504/ijmic.2020.10032851. Epub 2020 Oct 13.
We present a system identification technique for the characterisation of the linearity and dynamic response of a PSOL valve and its corresponding electronic control unit (ECU) using bandlimited white noise, as well as pseudo random "non-sum non-difference" (NSND) waveforms consisting of mutually prime frequencies to mitigate the effects of nonlinear distortions. The parameters of several transfer function models were simultaneously estimated from the voltage-flow frequency response using a nonlinear gradient descent technique. Candidate transfer function models were assessed using the mean squared residual (MSR) criterion and the corrected Akaike information criterion (AICc). The MSR yielded a transfer function consisting of 10 poles and 9 zeros, while the AICc yielded a simpler transfer function consisting of 5 poles and 3 zeros. Monte Carlo analysis demonstrated fragile stability for the MSR-selected model with respect to varying parameter values within estimated uncertainties, yet a robust stability for the AICc-selected model.
我们提出了一种系统识别技术，用于使用带限白噪声以及由互质频率组成的伪随机“非和非差”（NSND）波形来表征PSOL阀及其相应电子控制单元（ECU）的线性度和动态响应，以减轻非线性失真的影响。使用非线性梯度下降技术从电压-流量频率响应中同时估计了几个传递函数模型的参数。使用均方残差（MSR）准则和修正的赤池信息准则（AICc）对候选传递函数模型进行评估。MSR得到了一个由10个极点和9个零点组成的传递函数，而AICc得到了一个更简单的由5个极点和3个零点组成的传递函数。蒙特卡罗分析表明，对于MSR选择的模型，在估计不确定性范围内参数值变化时稳定性较弱，但对于AICc选择的模型则具有稳健的稳定性。
