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  • 《電子技術應用》
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    基于自組織模糊神經網絡的大功率LED調光模型
    2021年電子技術應用第12期
    李紀賓,饒歡樂,王 晨,錢依凡,洪哲揚
    杭州電子科技大學 自動化學院,浙江 杭州310018
    摘要: 大功率LED光度輸出不僅與操作電流大小有關,且受傳熱過程的時滯時變不確定因素影響難以預測。針對傳統機理建模存在參數提取困難、模型適應性弱等缺點,提出基于模糊神經網絡建模算法,從而構建以操作電流、熱沉溫度、環境溫度為輸入,光通量為輸出的調光模型。模型結構和參數依據在線數據進行調整,通過遞推學習,模糊規則得到增量式完善,進而不斷逼近實際動態過程。結果表明,利用該方法構建的調光模型與參考模型理論值相對誤差小于3%,與其他模型相比,結構更加緊湊,預測精度更高。
    中圖分類號: TN364+.2
    文獻標識碼: A
    DOI:10.16157/j.issn.0258-7998.201125
    中文引用格式: 李紀賓,饒歡樂,王晨,等. 基于自組織模糊神經網絡的大功率LED調光模型[J].電子技術應用,2021,47(12):105-109.
    英文引用格式: Li Jibin,Rao Huanle,Wang Chen,et al. Dimming model of high-power LED based on self-organizing fuzzy neural network[J]. Application of Electronic Technique,2021,47(12):105-109.
    Dimming model of high-power LED based on self-organizing fuzzy neural network
    Li Jibin,Rao Huanle,Wang Chen,Qian Yifan,Hong Zheyang
    School of Automation,Hangzhou Dianzi University,Hangzhou 310018,China
    Abstract: The luminosity output of high-power LED system is not only related to the current, but also hard to be predicted due to the uncertain nonlinear characters of thermal process. In view of the difficulties in extracting the parameters of the mechanism model and poor adaptability, an online modeling method was proposed to construct a fuzzy neural network with ambient temperature, heat sink temperature and operating current as input,and luminous flux as output. The model structure is self-organized and adjusted according to clustering analysis and error evaluation criteria. EKF algorithm and recursive least square method are used to learn network parameters. Through recursive learning, the rule is improved incrementally so that the model can approximate the actual system process as fast as possible. Validity of the algorithm is verified in a typical nonlinear system. Results show that the relative error between the theoretical values of the photometric prediction model and the reference model is less than 3%. Comparing with other model, this model has more compact structure and better generalization performance.
    Key words : high-power LED;PET model;self-organizing fuzzy neural network;structure identification;parameter learning

    0 引言

        相較于傳統光源,大功率LED具有高光效和靈活可控等優勢,在提供交互式或動態照明方面頗具潛力,如建筑照明[1]、太陽光模擬器[2]等。這類光源通常要求光度輸出寬范圍動態可調,并且快速達到預定的精度要求。盡管LED自身開關特性可達兆赫茲,但由于系統散熱存在時滯、時變不確定特性,使得光度輸出規律難以預測。構建可分析、可計算和執行的調光模型對實現更加精細化的調光控制具有重要意義。

        經典光電熱[3]理論表明LED結溫、光通量、電流存在多參數耦合關系。而后,Tao[4]等人通過機理分析,構建動態光電熱模型,用于計算光通量輸出隨系統溫升的衰減變化。文獻[5]~[6]考慮環境溫度的熱因素影響,構建不同操作功率下的線性擾動模型,設計了溫度前饋補償器,以保證光度的恒定輸出。文獻[7]建立了基于狀態空間表達的線性預測模型,便于移植到低成本控制器中去。文獻[8]采用多項式插值方法辨識不同驅動電流下的傳遞函數的零極點增益,構建了線性參數時變模型,但該方法需預先設置整個工作范圍的操作條件,計算量較大。盡管LED物理機制明確,但多數模型[3-6]基于等效阻容網絡分析,部分物理量(如結溫)并不易于測量,且模型采用離線設計,在長時運行或環境變化較大的條件下將存在失配問題。




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    作者信息:

    李紀賓,饒歡樂,王  晨,錢依凡,洪哲揚

    (杭州電子科技大學 自動化學院,浙江 杭州310018)




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