论文标题
基于GPROMS平台的快速型MPC工具
A rapid-prototype MPC tool based on gPROMS platform
论文作者
论文摘要
本文介绍了基于GPROMS平台的快速型模型预测控制（MPC）工具，并支持整个MPC设计工作流程。 GPROMS-MPC工具不仅可以直接与基于第一原则的GPROM模型进行闭环模拟相互作用，而且还利用其数学信息来得出基本上通过线性化技术得出简化的面向控制的模型。它可以继承基于第一原则的GPROM模型的解释性，这与Paroc框架不同，在该框架中，基于GPROMS模拟数据，从黑框系统识别获得了面向控制的模型。 GPROMS-MPC工具允许用户选择何时线性化，例如每个采样时间（连续线性化）或一些特定点以获得一个或多个良好的线性模型。 GPROMS-MPC工具实现了我们先前的无施工CDAL和在线参数活动QPOases算法，分别解决了可能连续的线性化或高状态差异案例，分别解决了稀疏或凝结的MPC问题公式。我们的CDAL算法也不矩阵且无库，因此支持嵌入式的C代码生成。经过许多示例验证该工具，在这里，我们仅显示一个示例来研究不同的MPC方案的性能。
This paper presents a rapid-prototype Model Predictive Control (MPC) tool based on the gPROMS platform, with the support for the whole MPC design workflow. The gPROMS-MPC tool can not only directly interact with a first-principle-based gPROMS model for closed-loop simulations but also utilizes its mathematical information to derive simplified control-oriented models, basically via linearization techniques. It can inherit the interpretability of the first-principle-based gPROMS model, unlike the PAROC framework in which the control-oriented models are obtained from black-box system identification based on gPROMS simulation data. The gPROMS-MPC tool allows users to choose when to linearize such as at each sampling time (successive linearization) or some specific points to obtain one or multiple good linear models. The gPROMS-MPC tool implements our previous construction-free CDAL and the online parametric active-set qpOASES algorithms to solve sparse or condensed MPC problem formulations, respectively, for possible successive linearization or high state-dimension cases. Our CDAL algorithm is also matrix-free and library-free, thus supporting embedded C-code generation. After many example validations of the tool, here we only show one example to investigate the performance of different MPC schemes.
