基于多重经验模型的非线性过程系统的经济模型预测控制

Anas Alanqar, M. Ellis, P. Christofides
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引用次数: 3

摘要

经济模型预测控制(EMPC)是一种将经济优化与反馈控制紧密结合的反馈控制技术,它是一种用表示过程经济的目标函数来制定的预测控制方案。顾名思义,EMPC需要一个可用的动态模型来计算其控制动作,这样的模型可以通过应用第一性原理或通过系统识别技术来获得。然而,在工业实践中,通常很难获得该过程的精确第一性原理模型。在此基础上,本文设计了基于lyapunov的经济模型预测控制(LEMPC),采用多个线性经验模型。与仅使用单一经验线性模型相比,使用不同的模型可以更准确地预测更大状态空间区域内非线性系统的行为。将该方案应用于一个化学过程实例,证明了其闭环稳定性和性能特性以及显著的计算优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Economic model predictive control of nonlinear process systems using multiple empirical models
Economic model predictive control (EMPC) is a feedback control technique that attempts to tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first-principles or though system identification techniques. However, in industrial practice, it may be difficult in general to obtain an accurate first-principles model of the process. Motivated by this, in the present work, Lyapunov-based economic model predictive control (LEMPC) is designed with multiple linear empirical models. The different models are used to more accurately predict the behavior of a nonlinear system over a larger state-space region compared to using a single empirical linear model only. The LEMPC scheme is applied to a chemical process example to demonstrate its closed-loop stability and performance properties as well as significant computational advantages.
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