基于双线性laguerre模型的非线性预测控制

Sana Achek, T. Garna, Kais Bouzrara
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引用次数: 0

摘要

在本文中,我们提出了一种新的非线性模型表示称为双线性-拉盖尔模型发展新的非线性模型预测控制(NMPC)。后者是由于双线性模型在Laguerre标准正交基上的扩展,使得能够识别与经典双线性模型相比参数数量显著减少的一大类非线性系统。通过利用双线性- laguerre模型的简单递归表示,利用最小化关于传感器和执行器约束的二次准则的经典技术应用预测控制策略。数值仿真算例说明了该控制器在考虑物理约束条件下的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nonlinear predictive control based on the bilinear-Laguerre model
In this paper, we propose to develop a new Nonlinear Model Predictive Control (NMPC) using a new nonlinear model representation entitled bilinear-Laguerre model. This latter is resulting from the expansion of the bilinear model on Laguerre orthonormal bases which enables to identify a large class of nonlinear systems with significant parameter number reduction with respect to the classical bilinear model. By exploiting the simple recursive representation of the bilinear-Laguerre model, the predictive control strategy is applied using the classical technique that minimizes a quadratic criterion with respect to constraints due to sensors and actuators. The effectiveness of the proposed controller that takes into account physical constraints is illustrated by a numerical simulation example.
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