| 11 | 0 | 21 |
| 下载次数 | 被引频次 | 阅读次数 |
为了满足间接蒸发冷却系统非线性和时变不确定的控制要求，本文将模糊控制与神经网络算法融合一起组成模糊神经网络控制器，再与常规比例积分微分（PID）控制器结合，构建模糊神经网络-PID(FNN-PID)。利用模糊控制良好的非线性控制优势，以及神经网络的强大学习能力，自适应特性，实现对PID参数的实时在线整定，并建立间接蒸发冷却温度模糊神经网络PID控制系统数学模型，利用MATLAB/Simulink进行仿真。仿真结果表明：阶跃响应对比时，FNN-PID控制器相较于常规PID控制器，超调量仅为0.1%，下降了18.9%，调节时间节省了249 s。抗干扰对比时，FNN-PID控制器收敛迅速且无振荡情况。自适应对比中，FNN-PID控制器的超调量比常规PID控制器下降21%，调节时间缩短了116 s。综上所述，FNN-PID控制器可以更好地满足间接蒸发冷却装置温度控制要求。
Abstract：In order to meet the nonlinear and time-varying uncertain control requirements of indirect evaporative cooling system, fuzzy control and neural network algorithm are combined to form a fuzzy neural network controller in this paper, and then combines the controller with the conventional proportional integral derivative(PID) controller to construct a fuzzy neural network-PID(FNN-PID) controller. Taking advantage of the good nonlinear control of fuzzy control, as well as the strong learning ability and adaptive characteristics of the neural network, the real-time online tuning of PID parameters is realized, and the mathematical model of the PID control system of the indirect evaporative cooling temperature fuzzy neural network is established, and the simulation is carried out by MATLAB/Simulink. The simulation results show that compared with the conventional PID controller, the overshoot of the FNN-PID controller is only 0.1%, which is reduced by 18.9%, and the adjustment time is saved by 249 s. In the anti-interference comparison, the FNN-PID controller converges quickly and has no oscillation. In the adaptive comparison, the overshoot of the FNN-PID controller is reduced by 21% compared with the conventional PID controller, and the adjustment time is shortened by 116 s. In summary, the FNN-PID controller can better meet the temperature control requirements of indirect evaporative cooling devices.
[1]MASANET E, SHEHABI A, LEI N, et al. Recalibrating global data center energy-use estimates[J]. Science, 2020,367:984-986.
[2]EBRAHIMI K, JONES F G, FLEISCHER S A. A review of data center cooling technology, operating conditions and the corresponding low-grade waste heat recovery opportunities[J]. Renewable and Sustainable Energy Reviews, 2014, 31:622-638.
[3]MITCHELL-JACKSON J, KOOMEY J G, BLAZEK M.Data center power requirements:measurements from Silicon Valley[J]. Energy, 2003, 28(8):837-850.
[4]LUO Y, ANDRESEN J, CLARKE H, et al. A decision support system for waste heat recovery and energy efficiency improvement in data centres[J]. Applied Energy,2019, 250:1217-1224.
[5]李慧.基于图卷积神经网络的数据中心冷却模型预测控制优化方法研究[J].制冷技术, 2024, 44(4):72-78.
[6]黄翔,强天伟,熊理.组合式蒸发冷却空调机组自控设计[J].暖通空调, 2010, 40(9):127-130.
[7]刘功银,葛芸芸,严小永.西气东输二线天然气分输控制的探讨与优化[J].仪器仪表用户, 2016, 23(10):31-33.
[8]CHEN Y, YAN H, LUO Y, et al. A proportional-integral(PI)law based variable speed technology for temperature control in indirect evaporative cooling system[J]. Applied Energy, 2019, 251(1):13390.
[9]LI E H, LI Y Z, LI J X, et al. An investigation on fuzzy incremental control strategy of water membrane evaporator cooling loop for mars spacesuit[J]. Acta Astronautica, 2021, 182:66-76.
[10]许玉忠.基于PLC的模糊PID水电厂蒸发冷却空调控制系统[J].水电与新能源, 2016(6):15-18.
[11]ASFAHAN H M, SAJJAD U, SULTAN M, et al. Artificial intelligence for the prediction of the thermal performance of evaporative cooling systems[J]. Energies, 2021, 14:39-46.
[12]SUN T, HUANG X, LIANG C, et al. Prediction and analysis of dew point indirect evaporative cooler performance by artificial neural network method[J].Energies, 2022, 15(13):4673.
[13]SUN T, SUN H, CHEN Y, et al. Development of artificial neural network models for indirect evaporative coolers in multi-climate regions based on field measurement[J].Building Services Engineering Research and Technology,2023, 44(5):519-536.
[14]徐红明,吴震,周波,等.船舶柴油机转速模糊神经网络PID智能控制[J].舰船科学技术, 2022, 44(21):101-105.
[15]蒋美云.船舶障碍避让系统中模糊神经网络多步预测模型设计[J].舰船科学技术, 2019, 41(8):178-180.
[16]屈宽生,李明哲.基于计算机模糊神经网络的船舶主机温度控制与仿真[J].舰船科学技术, 2020, 42(20):82-84.
[17]顾冬华,周振.无菌包装预热系统温度控制方法研究[J].包装工程, 2019, 40(23):162-166.
[18]程宗政,施一萍,张金立,等.基于模糊神经网络PID算法的液位控制系统研究[J].电子测量技术, 2019,42(9):29-34.
[19]赵世海,韩雪.基于模糊神经网络PID的焙烘机温度控制[J].天津工业大学学报, 2019, 38(4):83-88.
[20]朱敏,赵聪聪,臧昭宇.基于改进型模糊神经网络PID阀位控制研究[J].现代制造工程, 2022(1):125-131.
[21]SONG Y, JIA F X, WANG X M, et al. Application of fuzzy neural network PID in laser-guided mortar projectile[J].Journal of Physics:Conference Series, 2021, 2113(1):012078.
[22]于号,李征涛,王智楷,等.间接蒸发冷却器在干湿模式下的冷却特性[J].化学工程, 2022, 50(6):38-42.
[23]彭鞍虹.通用变频器异步电动机的传递函数[J].鞍山钢铁学院学报, 2000(6):447-449.
[24]邱亮.基于阶跃辨识的PID自整定算法研究及其应用[D].上海:上海交通大学, 2013.
[25]刘金琨.先进PID控制MATLAB仿真[M].北京:电子工业出版社:2016.
基本信息:
中图分类号:TP183;TP308
引用信息:
[1]李强,高梦蝶,曹军.基于模糊神经网络比例积分微分算法的数据中心间接蒸发冷却控制方法研究[J].制冷技术,2025,45(06):18-25+46.
基金信息:
国家自然科学基金（No.22393954）
2025-12-31
2025-12-31