• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

PSO-BPNN-PID 控制器在营养液 EC 精准控制系统中的应用：应用研究。

Application of PSO-BPNN-PID Controller in Nutrient Solution EC Precise Control System: Applied Research.

机构信息

State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Lushan South Road, Yuelu District, Changsha 410082, China.

Guizhou Institute of Water Resources Science, Guiyang 550002, China.

出版信息

Sensors (Basel). 2022 Jul 24;22(15):5515. doi: 10.3390/s22155515.

DOI:10.3390/s22155515
PMID:35898019
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9330439/
Abstract

In this paper, we present a nutrient solution control system, designing a nutrient solution electrical conductivity (EC) sensing system composed of multiple long-range radio (LoRa) slave nodes, narrow-band Internet of Things (NB-IoT) master nodes, and a host computer, building a nutrient solution EC control model and using the particle swarm optimization (PSO) algorithm to optimize the initial weights of a back-propagation neural network (BPNN). In addition, the optimized best weights are put into the BPNN to adjust the proportional-integral-derivative (PID) control parameters Kp, Ki, and Kd so that the system performance index can be optimized. Under the same initial conditions, we input EC = 2 mS/cm and use the particle swarm optimization BP neural network PID (PSO-BPNN-PID) to control the EC target value of the nutrient solution. The optimized scale factors were Kp = 81, Ki = 0.095, and Kd = 0.044; the steady state time was about 43 s, the overshoot was about 0.14%, and the EC value was stable at 1.9997 mS/cm-2.0027 mS/cm. Compared with the BP neural network PID (BPNN-PID) and the traditional PID control approach, the results show that PSO-BPNN-PID had a faster response speed and higher accuracy. Furthermore, we input 1 mS/cm, 1.5 mS/cm, 2 mS/cm, and 2.5 mS/cm, respectively, and simulated and verified the PSO-BPNN-PID system model. The results showed that the fluctuation range of EC was 0.003 mS/cm0.119 mS/cm, the steady-state time was 40 s60 s, and the overshoot was 0.3%~0.14%, which can meet the requirements of the rapid and accurate integration of water and fertilizer in agricultural production.

摘要

在本文中，我们提出了一种营养液控制系统，设计了一种由多个远程无线电 (LoRa) 从节点、窄带物联网 (NB-IoT) 主节点和一台主机组成的营养液电导率 (EC) 感测系统，构建了营养液 EC 控制模型，并使用粒子群优化 (PSO) 算法优化了反向传播神经网络 (BPNN) 的初始权重。此外，将优化后的最佳权重输入 BPNN 以调整比例积分微分 (PID) 控制参数 Kp、Ki 和 Kd，从而优化系统性能指标。在相同的初始条件下，我们输入 EC = 2 mS/cm，并使用粒子群优化 BP 神经网络 PID (PSO-BPNN-PID) 控制营养液的 EC 目标值。优化后的比例因子为 Kp = 81，Ki = 0.095，Kd = 0.044；稳定时间约为 43 s，超调量约为 0.14%，EC 值稳定在 1.9997 mS/cm-2.0027 mS/cm。与 BP 神经网络 PID (BPNN-PID) 和传统 PID 控制方法相比，结果表明 PSO-BPNN-PID 具有更快的响应速度和更高的精度。此外，我们分别输入 1 mS/cm、1.5 mS/cm、2 mS/cm 和 2.5 mS/cm，对 PSO-BPNN-PID 系统模型进行了模拟和验证。结果表明，EC 的波动范围为 0.003 mS/cm0.119 mS/cm，稳定时间为 40 s60 s，超调量为 0.3%~0.14%，可满足农业生产中水肥快速准确一体化的要求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/6d96942eea01/sensors-22-05515-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/bd191c0f7054/sensors-22-05515-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/1630c105d24b/sensors-22-05515-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/c4217fc81980/sensors-22-05515-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/0577816c9370/sensors-22-05515-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/7d17bc000a79/sensors-22-05515-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/5d60331a48ba/sensors-22-05515-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/7ecf840a54f3/sensors-22-05515-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/e5831c2dfdf0/sensors-22-05515-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/81254eced5ed/sensors-22-05515-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/1f05f495cb07/sensors-22-05515-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/6043a309eed6/sensors-22-05515-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/6d96942eea01/sensors-22-05515-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/bd191c0f7054/sensors-22-05515-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/1630c105d24b/sensors-22-05515-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/c4217fc81980/sensors-22-05515-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/0577816c9370/sensors-22-05515-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/7d17bc000a79/sensors-22-05515-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/5d60331a48ba/sensors-22-05515-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/7ecf840a54f3/sensors-22-05515-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/e5831c2dfdf0/sensors-22-05515-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/81254eced5ed/sensors-22-05515-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/1f05f495cb07/sensors-22-05515-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/6043a309eed6/sensors-22-05515-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c6f/9330439/6d96942eea01/sensors-22-05515-g012.jpg

相似文献

1
Application of PSO-BPNN-PID Controller in Nutrient Solution EC Precise Control System: Applied Research.PSO-BPNN-PID 控制器在营养液 EC 精准控制系统中的应用：应用研究。
Sensors (Basel). 2022 Jul 24;22(15):5515. doi: 10.3390/s22155515.
2
An Internet of Things-Oriented Adaptive Mutation PSO-BPNN Algorithm to Assist the Construction of Entrepreneurship Evaluation Models for College Students.面向物联网的自适应变异 PSB-BPNN 算法辅助大学生创业评价模型构建。
Comput Intell Neurosci. 2021 Dec 15;2021:3371383. doi: 10.1155/2021/3371383. eCollection 2021.
3
A Self-Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM-BPNN.基于 SOM-BPNN 的多工况汽车故障自诊断方法。
Comput Intell Neurosci. 2021 Nov 3;2021:6801161. doi: 10.1155/2021/6801161. eCollection 2021.
4
Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO-BPNN Algorithm.基于 PSO-BPNN 算法的突发金融灾害智能应急避险数字经济研究。
Comput Intell Neurosci. 2021 Nov 30;2021:7708422. doi: 10.1155/2021/7708422. eCollection 2021.
5
PSO-BP Neural Network-Based Strain Prediction of Wind Turbine Blades.基于粒子群优化-反向传播神经网络的风力发电机叶片应变预测
Materials (Basel). 2019 Jun 12;12(12):1889. doi: 10.3390/ma12121889.
6
Wireless Sensor Network Congestion Control Based on Standard Particle Swarm Optimization and Single Neuron PID.基于标准粒子群优化和单神经元 PID 的无线传感器网络拥塞控制
Sensors (Basel). 2018 Apr 19;18(4):1265. doi: 10.3390/s18041265.
7
Performance evaluation of PSO-PID and PSO-FLC for continuum robot's developed modeling and control.用于连续体机器人的已开发建模与控制的粒子群优化-比例积分微分（PSO-PID）和粒子群优化-模糊逻辑控制器（PSO-FLC）的性能评估
Sci Rep. 2024 Jan 6;14(1):733. doi: 10.1038/s41598-023-50551-0.
8
An Apple Fungal Infection Detection Model Based on BPNN Optimized by Sparrow Search Algorithm.基于麻雀搜索算法优化的 BPNN 的苹果真菌病检测模型。
Biosensors (Basel). 2022 Aug 28;12(9):692. doi: 10.3390/bios12090692.
9
Optimization of PID control parameters for marine dual-fuel engine using improved particle swarm algorithm.基于改进粒子群算法的船用双燃料发动机PID控制参数优化
Sci Rep. 2024 Jun 3;14(1):12681. doi: 10.1038/s41598-024-63253-y.
10
Parameters-tuning of PID controller for automatic voltage regulators using the African buffalo optimization.基于非洲水牛优化算法的自动电压调节器PID控制器参数整定
PLoS One. 2017 Apr 25;12(4):e0175901. doi: 10.1371/journal.pone.0175901. eCollection 2017.

本文引用的文献

1
A Design of FPGA-Based Neural Network PID Controller for Motion Control System.一种用于运动控制系统的基于现场可编程门阵列的神经网络比例积分微分控制器设计。
Sensors (Basel). 2022 Jan 24;22(3):889. doi: 10.3390/s22030889.
2
An Inverse Neural Controller Based on the Applicability Domain of RBF Network Models.基于径向基函数（RBF）网络模型适用域的逆神经控制器
Sensors (Basel). 2018 Jan 22;18(1):315. doi: 10.3390/s18010315.
3
Neural Network-Based Self-Tuning PID Control for Underwater Vehicles.基于神经网络的水下航行器自整定PID控制
Sensors (Basel). 2016 Sep 5;16(9):1429. doi: 10.3390/s16091429.
4
Temperature effects and compensation-control methods.温度效应和补偿控制方法。
Sensors (Basel). 2009;9(10):8349-76. doi: 10.3390/s91008349. Epub 2009 Oct 21.