Robust nonlinear H∞ control of hyperbolic distributed parameter systems

被引：25

作者：

Aggelogiannaki, Eleni ^{[1
]}

Sarimveis, Haralambos ^{[1
]}

机构：

[1] Natl Tech Univ Athens, Sch Chem Engn, Athens 15780, Greece

来源：

CONTROL ENGINEERING PRACTICE | 2009年 / 17卷 / 06期

关键词：

Hyperbolic distributed parameter systems; Radial basis function neural networks; H-infinity control; Robust control; Thermal systems; MODEL-PREDICTIVE CONTROL; FEEDBACK-CONTROL;

D O I：

10.1016/j.conengprac.2008.11.005

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A radial basis function (RBF) neural network model is developed for the identification of hyperbolic distributed parameter systems (DPSs). The empirical model is based only on process input-output data and is used for the estimation of the controlled variables at multiple spatial locations. The produced nonlinear model is transformed to a nonlinear state-space formulation, which in turn is used for deriving a robust H-infinity control law. The proposed methodology is applied to a long duct for the flow-based control of temperature distribution. The performance of the proposed method is illustrated by comparing it with conventional control strategies. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：723 / 732

页数：10

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