Nonlinear MPC based on a Volterra series model for greenhouse temperature control using natural ventilation

被引：81

作者：

Gruber, J. K. ^{[1
]}

Guzman, J. L. ^{[2
]}

Rodriguez, F. ^{[2
]}

Bordons, C. ^{[1
]}

Berenguel, M. ^{[2
]}

Sanchez, J. A. ^{[2
]}

机构：

[1] Univ Seville, Dep Ingn Sistemas & Automat, Escuela Super Ingn, Seville 41092, Spain

[2] Univ Almeria, Dep Lenguajes & Computac, Almeria 04120, Spain

来源：

CONTROL ENGINEERING PRACTICE | 2011年 / 19卷 / 04期

关键词：

Nonlinear model predictive control; Greenhouse climate control; Volterra series model; Identification; PREDICTIVE CONTROL;

D O I：

10.1016/j.conengprac.2010.12.004

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Suitable environmental conditions are a fundamental issue in greenhouse crop growth and can be achieved by advanced climate control strategies. In different climatic zones, natural ventilation is used to regulate both the greenhouse temperature and humidity. In mild climates, the greatest problem faced by far in greenhouse climate control is cooling, which, for dynamical reasons, leads to natural ventilation as a standard tool. This work addresses the design of a nonlinear model predictive control (NMPC) strategy for greenhouse temperature control using natural ventilation. The NMPC strategy is based on a second-order Volterra series model identified from experimental input/output data of a greenhouse. These models, representing the simple and logical extension of convolution models, can be used to approximate the nonlinear dynamic effect of the ventilation and other environmental conditions on the greenhouse temperature. The developed NMPC is applied to a greenhouse and the control performance of the proposed strategy will be illustrated by means of experimental results. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：354 / 366

页数：13

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