A common process control application is the cascaded two-tank system, where the level is controlled in the second tank. A nonlinear system identification approach is presented in this work to predict the model structure parameters that minimize the difference between the estimated and measured data, using benchmark datasets. The general suggested structure consists of a static nonlinearity in cascade with a linear dynamic filter in addition to colored noise element. A one-step ahead prediction error-based technique is proposed to estimate the model. The model is identified using a separable least squares optimization, where only the parameters that appear nonlinearly in the output of the predictor are solved using a modified Levenberg–Marquardt iterative optimization approach, while the rest are fitted using simple least squares after each iteration. Finally, MATLAB simulation examples using benchmark data are included.

中文翻译：

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Hammerstein Box-Jenkins系统识别级联坦克基准系统

常见的过程控制应用是级联双罐系统，其中液位在第二个储罐中进行控制。在这项工作中提出了一种非线性系统识别方法，以使用基准数据集预测模型结构参数，以最大程度地减少估计数据和测量数据之间的差异。一般建议的结构包括级联的静态非线性和线性动态滤波器，以及有色噪声元素。提出了一种基于单步超前预测误差的技术来估计模型。使用可分离的最小二乘法优化来识别模型，其中使用改进的Levenberg-Marquardt迭代优化方法仅求解在预测变量输出中非线性出现的参数，其余的则在每次迭代后使用简单的最小二乘法进行拟合。最后，包括使用基准数据的MATLAB仿真示例。