参数化NMPC中数据驱动代价函数辨识的应用
On the use of Data-Driven Cost Function Identification in Parametrized NMPC
摘要：
本文提出了一个框架，完整的数值研究的可行性约束非线性模型预测控制（NMPC）的设计使用数据驱动模型的代价函数。虽然这个想法还很不成熟，但本文提出了一个完整的实现，使用GitHub存储库上免费提供的python模块。此外，讨论通过数据驱动的建模获得控制的不同方式，提出了从业者可能感兴趣的。
英文摘要：
In this paper, a framework with complete numerical investigation is proposed regarding the feasibility of constrained Nonlinear Model Predictive Control (NMPC) design using Data-Driven model of the cost function. Although the idea is very much in the air, this paper proposes a complete implementation using python modules that are made freely available on a GitHub repository. Moreover, a discussion regarding the different ways of deriving control via data-driven modeling is proposed that can be of interest to practitioners.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
