Mathematical modeling of a system plays a vital role in controller design, tuning to meet the desired specifications and process parameter optimization. In this paper, different identification algorithms are implemented in the four‐tank system to demonstrate the superiority of the recommended subspace system identification method (Numeric Algorithms for Subspace State Space System Identification [N4SID]) for the different input design. A system identification method was utilized to identify the model of a dynamic system. It is modeled using the First‐Principle Method (FPM), Prediction Error Method (PEM) and Numeric Algorithms for N4SID method. The identification is performed through MATLAB. The plant input and output is acquired through LabVIEW. The developed models are validated with a new dataset based on the model performance specifications: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Best Fit Rate (BFR). The results show that the proposed N4SID method with the amplitude‐modulated pseudorandom binary signal (APRBS) signal is better than the other methods with different inputs. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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使用黑箱和白箱模型方法对液位控制过程的四罐系统进行建模和验证

系统的数学建模在控制器设计，调整以满足所需规格和过程参数优化方面起着至关重要的作用。本文在四缸系统中实现了不同的识别算法，以证明推荐的子空间系统识别方法（用于子空间状态空间系统识别的数字算法[N4SID]）对于不同输入设计的优越性。系统识别方法被用来识别动态系统的模型。它使用第一原理方法（FPM），预测误差方法（PEM）和用于N4SID的数值算法进行建模。识别是通过MATLAB执行的。工厂的输入和输出通过LabVIEW获取。根据模型性能规格，使用新的数据集对开发的模型进行验证：平均绝对误差（MAE），均方根误差（RMSE）和最佳拟合率（BFR）。结果表明，所提出的带有调幅伪随机二进制信号（APRBS）信号的N4SID方法要优于其他具有不同输入的方法。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。