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Black- and white-box approaches for cascaded tanks benchmark system identification

机译：黑盒和白盒级联储罐基准系统识别方法

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This contribution consists of the identification and comparison of different models for a non-linear system: the Cascaded Tanks system. The identification of this system is challenging due to the combination of soft and hard non-linearities. Model structures with different levels of flexibility and prior knowledge are compared. The most simple ones are linear black-box models. They are extended to become non-linear black-box models, whose performances are compared with the linear ones. A second track is the investigation of a series of models with increasing complexity based on physical prior knowledge. Results show that while linear black-box models perform good in prediction, a fairly precise description of the non-linear effects is needed to achieve good performances in simulation. All models have been estimated and validated using benchmark data from a real cascaded tanks system. The contribution represents also an overview on how standard modelling techniques perform on a real identification problem.
机译：这种贡献包括识别和比较非线性系统的不同模型：级联储罐系统。由于软性和硬性非线性的结合，该系统的识别具有挑战性。比较了具有不同灵活性和先验知识的模型结构。最简单的是线性黑盒模型。它们被扩展为非线性黑盒模型，其性能可与线性黑盒模型进行比较。第二条路线是基于物理先验知识研究一系列模型，这些模型的复杂性不断提高。结果表明，尽管线性黑匣子模型在预测中表现良好，但仍需要对非线性效应进行相当精确的描述才能在仿真中获得良好的性能。所有模型均使用来自真实级联储罐系统的基准数据进行了估算和验证。该文稿还代表了对标准建模技术如何处理实际识别问题的概述。

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