文章基本信息

标题：Adaptive Feedback Linearization-based Predictive Control for Greenhouse Temperature
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作者：Chen Lijun ; Du Shangfeng ; Liang Meihui 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：17
页码：784-789
DOI：10.1016/j.ifacol.2018.08.100
语种：English
出版社：Elsevier
摘要：AbstractFor a greenhouse temperature model with high non-linearity, strong uncertainties and varying parameters, a general framework for adaptive feedback linearization-based predictive controller design for indoor temperature system is presented. Firstly, a first-principle physical greenhouse temperature model is described and transferred to a standard affine non-linear system. Then, the well-known unscented Kalman filter is used for automatic, online estimation of parameter and state in the temperature model, which allows the control strategy to be applied in an adaptive way. In addition, based on the adaptive greenhouse temperature system, model predictive control with state feedback linearization is incorporated to address the reference deviation and energy consumption. At last, various simulation tests are applied to gain some insight into the performance of the developed approach as compared to the predictive control combining feedback linearization without the unscented Kalman filter estimation, resulting in the greenhouse temperature tracking root mean square errors as 1.0 °C and 1.2 °C, respectively. The simulation results show that the proposed approach can provide fast and accurate tracking of set points adaptively to time-varying phenomena and can improve the energy efficiency of the greenhouse temperature control.
关键词：KeywordsGreenhouse temperaturefeedback linearizationunscented Kalman filteronline estimationpredictive control