Economic model predictive control (EMPC) has attracted significant attention in recent years and is recognized as a promising advanced process control method for the next generation smart manufacturing. It can lead to improving economic performance but at the same time increases the computational complexity significantly. Model approximation has been a standard approach for reducing computational complexity in process control. In this work, we perform a study on three types of representative model approximation methods applied to EMPC, including model reduction based on available first-principle models (e.g., proper orthogonal decomposition), system identification based on input-output data (e.g., subspace identification) that results in an explicitly expressed mathematical model, and neural networks based on input-output data. A representative algorithm from each model approximation method is considered. Two processes that are very different in dynamic nature and complexity were selected as benchmark processes for computational complexity and economic performance comparison, namely an alkylation process and a wastewater treatment plant (WWTP). The strengths and drawbacks of each method are summarized according to the simulation results, with future research direction regarding control oriented model approximation proposed at the end.

中文翻译：

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模型逼近方法应用于经济MPC的比较研究

经济模型预测控制（EMPC）近年来引起了广泛关注，被公认为下一代智能制造的一种有前途的先进过程控制方法。它可以提高经济性能，但同时显着增加了计算复杂性。模型近似已成为降低过程控制计算复杂性的标准方法。在这项工作中，我们对应用于 EMPC 的三种代表性模型近似方法进行了研究，包括基于可用的第一原理模型（例如，适当的正交分解）的模型简化，基于输入输出数据的系统识别（例如，子空间识别）产生明确表达的数学模型，以及基于输入输出数据的神经网络。考虑了来自每种模型近似方法的代表性算法。选择两个在动态性质和复杂性上非常不同的过程作为计算复杂性和经济性能比较的基准过程，即烷基化过程和废水处理厂 (WWTP)。根据仿真结果总结了每种方法的优缺点，最后提出了面向控制的模型逼近的未来研究方向。