GEKKO Optimization Suite
GEKKO Optimization Suite
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DOI：
10.3390/pr6080106
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发表时间：
2018-08-01
期刊：
影响因子：
3.5
通讯作者：
Hedengren, John D.
中科院分区：
文献类型：
--
作者：
Beal, Logan D. R.;Hill, Daniel C.;Hedengren, John D.
本文介绍了GEKKO作为一个用于Python的优化套件。GEKKO专门用于混合整数、非线性以及微分代数方程（DAE）问题的动态优化。通过融合典型代数建模语言（AML）和最优控制包的方法，GEKKO极大地促进了诸如非线性模型预测控制（NMPC）、实时优化（RTO）、移动时域估计（MHE）和动态模拟等工具的开发和应用。GEKKO是一个面向对象的Python库，它提供模型构建、分析工具以及模拟和优化的可视化。在一个单一的包中，GEKKO提供模型降阶、一个用于数据协调/模型预测控制的面向对象库，以及集成的问题构建/求解/可视化。本文介绍了GEKKO优化套件，展示了GEKKO的方法以及它在AML和最优控制包中的独特地位，并列举了几个由GEKKO库实现的问题示例。
This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the development and application of tools such as nonlinear model predicative control (NMPC), real-time optimization (RTO), moving horizon estimation (MHE), and dynamic simulation. GEKKO is an object-oriented Python library that offers model construction, analysis tools, and visualization of simulation and optimization. In a single package, GEKKO provides model reduction, an object-oriented library for data reconciliation/model predictive control, and integrated problem construction/solution/visualization. This paper introduces the GEKKO Optimization Suite, presents GEKKO's approach and unique place among AMLs and optimal control packages, and cites several examples of problems that are enabled by the GEKKO library.