文章摘要
黄卓超,张伟,王亚刚.改进粒子群算法的啤酒灌装机液位控制PID参数整定[J].包装工程,2020,41(19):159-165.
HUANG Zhuo-chao,ZHANG Wei,WANG Ya-gang.PID Parameter Setting of Liquid Level Control for Beer Filling Machine Based on Improved Particle Swarm Algorithm[J].Packaging Engineering,2020,41(19):159-165.
改进粒子群算法的啤酒灌装机液位控制PID参数整定
PID Parameter Setting of Liquid Level Control for Beer Filling Machine Based on Improved Particle Swarm Algorithm
投稿时间:2019-12-16  修订日期:2020-10-10
DOI:10.19554/j.cnki.1001-3563.2020.19.023
中文关键词: 液位控制  PID控制  改进粒子群算法  Rosenbrock搜索法
英文关键词: liquid level control  PID control  improved particle swarm algorithm  Rosenbrock search method
基金项目:国家自然科学基金(11502145,61074087,61703277)
作者单位
黄卓超 上海理工大学 光电信息与计算机工程学院上海 200093 
张伟 上海理工大学 光电信息与计算机工程学院上海 200093 
王亚刚 上海理工大学 光电信息与计算机工程学院上海 200093 
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中文摘要:
      目的 整定最优的PID控制参数,对啤酒灌装机中贮液罐液位进行控制,以保证PID控制器能满足啤酒生产中的控制要求。方法 结合Rosenbrock搜索法和个体扰动策略来改进粒子群算法,并利用改进算法整定PID参数,最后将整定好参数的PID控制器用于控制液位对象;基于Matlab进行仿真实验,利用粒子群算法与文中方法做比较。结果 通过Matlab仿真验证,改进了粒子群算法整定的PID参数,其跟踪特性的调节时间为16.18 s,超调量为10.20%,IAE性能指标约为6.09;粒子群算法整定结果表明,跟踪特性的调节时间为27.72 s,超调量为26.90%,IAE性能指标约为7.23。结论 与原始粒子群算法相比,文中算法整定的3个PID参数在控制液位对象时综合性能评价指标更好,且能使系统平稳过渡,超调较小,响应速度快,调节时间快,其控制器性能能满足啤酒灌装机的生产要求。
英文摘要:
      The work aims to control the liquid level of the liquid storage tank in the beer filling machine by setting the optimum PID control parameters to ensure that the PID controller can meet the control requirements in beer production. The particle swarm algorithm was improved by Rosenbrock search method and individual disturbance, and the improved algorithm was used to set the PID parameters, and finally the PID controller with setting parameters was used to control the liquid level. The simulation experiment was carried out based on Matlab and the proposed algorithm was compared with the particle swarm algorithm. Through Matlab simulation verification, the PID parameters set by the improved particle swarm algorithm had a tracking characteristics adjustment time of 16.18 s, an overshoot amount of 10.20%, and an IAE performance index of about 6.09. For the particle swarm algorithm, the adjustment time of tracking characteristics was 27.72 s, the overshoot was 26.90% and the IAE performance index was about 7.23. Compared with the original particle swarm algorithm, 3 PID parameters set by the improved particle swarm algorithm in this paper have better comprehensive performance evaluation indicators when controlling the liquid level, and makes the system transition smoothly with less overshoot, fast response speed and fast adjustment time, so that the controller performance can meet the production requirements of beer filling machines.
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