基于灰色粒子群算法的温室环境多目标优化控制
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农业部引进国际先进科学技术“948”项目(2015–Z44);农业部农业物联网技术集成与应用重点实验室开放基金(2016KL05);安徽农业大学引进与稳定人才科研项目(wd2015–05)


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    摘要:

    引入人工控制因素,以扩展的自回归模型(ARX)为基础,构建茶树育苗的温度、相对湿度及耗电量多目标模型函数,采用灰色关联理论和粒子群优化算法(PSO),面向茶树育苗温室环境模型进行多目标优化控制。仿真结果表明,运用多目标灰色PSO算法将育苗温室内温度值从31.5 ℃降为24.51 ℃,相对湿度从47.2%提升为59.35%,耗电量降低17.6%。与线性加权和法、单目标PSO算法相比,选取多目标灰色PSO算法对温室进行优化,得到在开启遮阳与喷淋组合调控的情况下,经过20 min温室内温度和相对湿度调控,即可达到茶苗生长的要求。

    Abstract:

    On the basis of the extended autoregressive model (ARX), a multi–objective function of the temperature, the humidity and the economic cost was established by introducing the artificial control factors. Multi–objective fuction optimization for environmental control was carried out in the tea–growing seedling greenhouse by using the grey correlation theory and the particle swarm optimization algorithm (PSO). The simulation results showed that the energy consumption could reduced by 17.6% under the control of the multi–objective PSO as the temperature dropped from 31.5 ℃ to 24.51 ℃and the humidity increased from 47.2% to 59.35%. Comparison with the linear weighted sum method and the single objective PSO, it could regulate the temperature and the humidity to meet the requirements for the growth of tea seedlings in greenhouse within 20 minutes under the condition of opening sun shading and spraying conditions.

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张雪花,张武,李叶云,蔡芮莹,朱小倩.基于灰色粒子群算法的温室环境多目标优化控制[J].湖南农业大学学报:自然科学版,2017,43(2):.

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  • 在线发布日期: 2017-04-18
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