基于改进量子粒子群算法的纸浆浓度控制系统
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摘要
目的为了克服传统PID控制在具有大时滞性、非线性等特点的纸浆浓度控制系统中性能不足和参数调整困难等问题,研究参数在线调整的方法。方法在传统PID控制的基础上,结合量子粒子群仿生算法(QPSO),提出一种量子粒子群算法优化的传统PID控制器参数,并应用于纸浆浓度控制系统;同时对基本量子粒子群算法进行改进,引入交叉算子,并将该控制算法应用到纸浆浓度控制系统中,并与传统控制进行对比。结果与传统PID控制和基本量子粒子群优化的PID相比较,改进的优化算法能够得到更加令人满意的控制效果,具有系统超调量小、响应速度快、鲁棒性高等优良的性能。结论基于改进的量子粒子群优化算法的纸浆浓度控制系统可有效控制纸浆浓度,能够明显提高系统的控制精度等性能指标,更好地满足实际应用的要求。
The paper aims to research the method of online parameter adjustment to overcome the shortcomings of traditional PID control in pulp consistency control system with large time delay and non-linearity and the difficulty of parameter adjustment. Based on traditional PID control and in combination with quantum particle swarm optimization(QPSO) bionic algorithm, this paper proposed a traditional PID controller parameters optimized by QPSO and applied it to pulp concentration control system. At the same time, the basic QPSO algorithm was improved by introducing crossover operator, and the control algorithm was applied to pulp concentration control. The system was compared with traditional control. The result showed that compared with the traditional PID control and the basic quantum particle swarm optimization(QPSO) PID, the improved optimization algorithm could achieve more satisfactory control effect. The system had the advantages of small overshoot, fast response speed and high robustness. Pulp consistency control system based on improved quantum particle swarm optimization algorithm can effectively control pulp consistency, significantly improve the control accuracy and other performance indicators of the system, and better meet the requirements of practical application.
The paper aims to research the method of online parameter adjustment to overcome the shortcomings of traditional PID control in pulp consistency control system with large time delay and non-linearity and the difficulty of parameter adjustment. Based on traditional PID control and in combination with quantum particle swarm optimization(QPSO) bionic algorithm, this paper proposed a traditional PID controller parameters optimized by QPSO and applied it to pulp concentration control system. At the same time, the basic QPSO algorithm was improved by introducing crossover operator, and the control algorithm was applied to pulp concentration control. The system was compared with traditional control. The result showed that compared with the traditional PID control and the basic quantum particle swarm optimization(QPSO) PID, the improved optimization algorithm could achieve more satisfactory control effect. The system had the advantages of small overshoot, fast response speed and high robustness. Pulp consistency control system based on improved quantum particle swarm optimization algorithm can effectively control pulp consistency, significantly improve the control accuracy and other performance indicators of the system, and better meet the requirements of practical application.
引文
[1]黄亚南,张爱娟,胡慕伊.基于单神经元PSD的纸浆浓度控制算法研究[J].中国造纸,2016,35(5):46-50.HUANG Ya-nan,ZHANG Ai-juan,HU Mu-yi.Pulp Consistency Control Algorithm Based on Single Neuron Adaptive PSD[J].China Pulp&Paper,2016,35(5):46-50.
[2]邹伟,孙瑜,周海君.纸浆浓度的仿人智能PID控制[J].中国造纸,2005,24(8):44-46.ZOU Wei,SUN Yu,ZHOU Hai-jun.Simulated Human Intelligence PID Control for Pulp Consistency[J].China Pulp&Paper,2005,24(8):44-46.
[3]吴新生.纸浆浓度的模糊神经网络自适应PID控制[J].计算机测量与控制,2013,21(11):2969-2971.WU Xin-sheng.Fuzzy Neural Network Adaptive PIDControl of Pulp Consistency[J].Computer Measurement&Control,2013,21(11):2969-2971.
[4]欧艳华.基于神经网络的自适应PID控制器设计[J].机械设计与制造,2014,6(3):263-265.OU Yan-hua.The Design of Adaptive PID Controller Based on Neural Network[J].Machinery Design&Manufacture,2014,6(3):263-265.
[5]高玉琪.基于模糊PID的跳汰机排料控制的仿真实验研究[J].煤矿机械,2014(6):74-76.GAO Yu-qi.Simulation Study on Jigger Discharging System Based on Fuzzy PID Conrtol[J].Coal Mine Machinery,2014(6):74-76.
[6]孙俊,方伟,吴小俊,等.量子行为粒子群优化:原理及其应用[M].北京:清华大学出版社,2011.SUN Jun,FANG Wei,WU Xiao-jun,et al.Quantum Behaved Particle S-warm Optimization:Principles and Applications[M].Beijing:Tsing Hua University Press,2011.
[7]陈银环.基于BP神经网络PID的纸浆浓度自适应控制[J].包装工程,2018,39(1):146-150.CHEN Yin-huan.Adaptive Control of Pulp Concentration Based on BP Neural Network PID[J].Package Engineering,2018(1):146-150.
[8]汤伟,胡祥满,孙小乐.基于模拟退火粒子群混合算法的纸浆浓度控制系统[J].包装工程,2017,38(7):15-20.TANG Wei,HU Xiang-man,SUN Xiao-le.The Control System of Pulp Concentration Based on the Hybrid Algorithm of Simulated Annealed Particle Swarm Optimization[J].Packaging Engineering,2017,38(7):15-20.
[9]单文娟,汤伟,王孟效.神经网络分数阶PID控制器在纸浆浓度控制中的应用[J].中国造纸学报,2016,31(4):44-48.SHAN Wen-juan,TANG Wei,WANG Meng-xiao.Application of Fractional Order PID Controller Based on Neural Network to Pulp Consistency Control System[J].Transactions of China Pulp and Paper,2016,31(4):44-48.
[10]黄亚南,张爱娟,胡慕伊.基于免疫-单神经元PID算法的纸浆浓度控制[J].中国造纸学报,2016,31(3):30-35.HUANG Ya-nan,ZHANG Ai-juan,HU Mu-yi.Pulp Consistency Control Based on Immune-single Neuron Algorithm[J].Transactions of China Pulp and Paper,2016,31(3):30-35.
[11]乔莹莹,宋威,马伟.基于GA优化QPSO算法的文本聚类[J].计算机应用研究,2014,31(10):2912-2915.QIAO Ying-ying,SONG Wei,MA Wei.Text Clustering Based on QPSO Algorithm Optimized by GA[J].Application Research of Computers,2014,31(10):2912-2915.
[12]夏华凤,张中国,刘艳君.船舶锚机控制系统量子粒子群优化PID算法的研究[J].电工电气,2016,1(4):25-29.XIA Hua-feng,ZHANG Zhong-guo,LIU Yan-jun.Study on PID Algorithm Based on Quantum-Behaved Particle Swam Optimization Used in Ship Windlass Control System[J].Electrotechnics Electric,2016,1(4):25-29.
[13]朱三毛.基于QPSO算法的除铜过程锌粉添加量优化方法研究[D].长沙:中南大学,2014.ZHU San-mao,Optimization of Zinc Dust Addition for Copper Removal Process Based on QPSO Algorithm[D].Changsha:Central South University,2014.
[14]吕亭亭,马小平,陈力.基于遗传算法优化的跳汰机排料系统PID控制仿真[J].工况自动化,2013,39(1):68-70.LYU Ting-ting,MA Xiao-ping,CHEN Li.Simulation of PID Control of Jig Discharging System Optimized by Genetic Algorithm[J].Industry and Mine Automation,2013,39(1):68-70.
[15]霍延军.基于量子粒子群算法的PID参数自整定方法[J].微电子学与计算机,2012,29(10):194-197.HUO Yan-jun.PID Self Tuning Method Based on Quantum Behaved Particle Swarm Optimization[J].Microelectronics and Computer,2012,29(10):194-197.
[2]邹伟,孙瑜,周海君.纸浆浓度的仿人智能PID控制[J].中国造纸,2005,24(8):44-46.ZOU Wei,SUN Yu,ZHOU Hai-jun.Simulated Human Intelligence PID Control for Pulp Consistency[J].China Pulp&Paper,2005,24(8):44-46.
[3]吴新生.纸浆浓度的模糊神经网络自适应PID控制[J].计算机测量与控制,2013,21(11):2969-2971.WU Xin-sheng.Fuzzy Neural Network Adaptive PIDControl of Pulp Consistency[J].Computer Measurement&Control,2013,21(11):2969-2971.
[4]欧艳华.基于神经网络的自适应PID控制器设计[J].机械设计与制造,2014,6(3):263-265.OU Yan-hua.The Design of Adaptive PID Controller Based on Neural Network[J].Machinery Design&Manufacture,2014,6(3):263-265.
[5]高玉琪.基于模糊PID的跳汰机排料控制的仿真实验研究[J].煤矿机械,2014(6):74-76.GAO Yu-qi.Simulation Study on Jigger Discharging System Based on Fuzzy PID Conrtol[J].Coal Mine Machinery,2014(6):74-76.
[6]孙俊,方伟,吴小俊,等.量子行为粒子群优化:原理及其应用[M].北京:清华大学出版社,2011.SUN Jun,FANG Wei,WU Xiao-jun,et al.Quantum Behaved Particle S-warm Optimization:Principles and Applications[M].Beijing:Tsing Hua University Press,2011.
[7]陈银环.基于BP神经网络PID的纸浆浓度自适应控制[J].包装工程,2018,39(1):146-150.CHEN Yin-huan.Adaptive Control of Pulp Concentration Based on BP Neural Network PID[J].Package Engineering,2018(1):146-150.
[8]汤伟,胡祥满,孙小乐.基于模拟退火粒子群混合算法的纸浆浓度控制系统[J].包装工程,2017,38(7):15-20.TANG Wei,HU Xiang-man,SUN Xiao-le.The Control System of Pulp Concentration Based on the Hybrid Algorithm of Simulated Annealed Particle Swarm Optimization[J].Packaging Engineering,2017,38(7):15-20.
[9]单文娟,汤伟,王孟效.神经网络分数阶PID控制器在纸浆浓度控制中的应用[J].中国造纸学报,2016,31(4):44-48.SHAN Wen-juan,TANG Wei,WANG Meng-xiao.Application of Fractional Order PID Controller Based on Neural Network to Pulp Consistency Control System[J].Transactions of China Pulp and Paper,2016,31(4):44-48.
[10]黄亚南,张爱娟,胡慕伊.基于免疫-单神经元PID算法的纸浆浓度控制[J].中国造纸学报,2016,31(3):30-35.HUANG Ya-nan,ZHANG Ai-juan,HU Mu-yi.Pulp Consistency Control Based on Immune-single Neuron Algorithm[J].Transactions of China Pulp and Paper,2016,31(3):30-35.
[11]乔莹莹,宋威,马伟.基于GA优化QPSO算法的文本聚类[J].计算机应用研究,2014,31(10):2912-2915.QIAO Ying-ying,SONG Wei,MA Wei.Text Clustering Based on QPSO Algorithm Optimized by GA[J].Application Research of Computers,2014,31(10):2912-2915.
[12]夏华凤,张中国,刘艳君.船舶锚机控制系统量子粒子群优化PID算法的研究[J].电工电气,2016,1(4):25-29.XIA Hua-feng,ZHANG Zhong-guo,LIU Yan-jun.Study on PID Algorithm Based on Quantum-Behaved Particle Swam Optimization Used in Ship Windlass Control System[J].Electrotechnics Electric,2016,1(4):25-29.
[13]朱三毛.基于QPSO算法的除铜过程锌粉添加量优化方法研究[D].长沙:中南大学,2014.ZHU San-mao,Optimization of Zinc Dust Addition for Copper Removal Process Based on QPSO Algorithm[D].Changsha:Central South University,2014.
[14]吕亭亭,马小平,陈力.基于遗传算法优化的跳汰机排料系统PID控制仿真[J].工况自动化,2013,39(1):68-70.LYU Ting-ting,MA Xiao-ping,CHEN Li.Simulation of PID Control of Jig Discharging System Optimized by Genetic Algorithm[J].Industry and Mine Automation,2013,39(1):68-70.
[15]霍延军.基于量子粒子群算法的PID参数自整定方法[J].微电子学与计算机,2012,29(10):194-197.HUO Yan-jun.PID Self Tuning Method Based on Quantum Behaved Particle Swarm Optimization[J].Microelectronics and Computer,2012,29(10):194-197.