改进的RBF神经网络PID算法在电磁振机中的应用
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摘要
目的为了减少实际工作生产环境对智能组合秤称量精度的影响,增加电磁振动系统对环境的抗干扰性能。方法用RBF神经网络PID算法改进组合秤中的电磁振动系统,并在基础的RBF神经网络PID控制算法上引入动量因子的平方,减少拟合误差和参数调节过程中的震荡现象。结果与基础的RBF神经网络PID控制算法相比,改进后算法的收敛速度更快,拟合精确度更好;当仿真长度增加时,依然可以很好地逼近目标函数。结论改进后的算法使电磁振机的振幅和振动频率更加稳定,可以减少环境中噪音对其的干扰。
The work aims to reduce the impact of the working production environment on the weighing accuracy of the intelligent combination scale and increase the anti-jamming performance of the electromagnetic vibration system to the environment. The RBF neural network PID control algorithm was used to improve the electromagnetic vibration system in the production process. And the square of the momentum factor was added to the basic RBF neural network PID control algorithm. The empirical accumulation of parameter changes was considered to reduce the parameter adjustment.Compared with the basic RBF neural network PID control algorithm, the improved algorithm had faster convergence speed and better fitting accuracy. When the simulation length increased, the target function could still be well approximated. The improved algorithm makes the amplitude and vibration frequency of the electromagnetic vibration machine more stable. It can reduce the interference of noise in the environment.
The work aims to reduce the impact of the working production environment on the weighing accuracy of the intelligent combination scale and increase the anti-jamming performance of the electromagnetic vibration system to the environment. The RBF neural network PID control algorithm was used to improve the electromagnetic vibration system in the production process. And the square of the momentum factor was added to the basic RBF neural network PID control algorithm. The empirical accumulation of parameter changes was considered to reduce the parameter adjustment.Compared with the basic RBF neural network PID control algorithm, the improved algorithm had faster convergence speed and better fitting accuracy. When the simulation length increased, the target function could still be well approximated. The improved algorithm makes the amplitude and vibration frequency of the electromagnetic vibration machine more stable. It can reduce the interference of noise in the environment.
引文
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[12]于瀛,杨世忠,衡丽帆.遗传模拟退火整定PID算法的应用仿真研究[J].自动化应用,2018(3):70-72.YU Ying,YANG Shi-zhong,HENG Li-fan.Application Simulation of Genetic Simulated Annealing PIDAlgorithm[J].Automation,2018(3):70-72.
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[14]申超群,宰守香.基于RBF-PID的啤酒灌装机贮液缸内液位控制[J].包装工程,2017,38(5):45-48.SHEN Chao-qun,ZAI Shou-xiang.Liquid Level Control in Liquid Storage Tank of Beer Filling Machine Based on RBF-PID[J].Packaging Engineering,2017,38(5):45-48.
[15]LIN F J,HSIEH H J,CHOU P H,et al.Digital Signal Processor-based Cross-coupled Synchronous Control of Dual Linear Motors via Functional Link Radial Basis Function Network[J].Iet Control Theory&Applications,2012,5(4):552-564.
[2]袁梦,刘萍,佘勃,等.组合秤包装生产线分布式监控系统的软件设计[J].包装工程,2009,30(11):10-12.YUAN Meng,LIU Ping,SHE Bo,et al.Software Design of Distributed Monitoring System for Combination Scale Packaging Production Line[J].Packaging Engineering,2009,30(11):10-12.
[3]IMAHORI S,KARUNO Y,NAGAMOCHI H,et al.Kansei Engineering,Humans and Computers:Efficient Dynamic Programming Algorithms for Combinatorial Food Packing Problems[J].International Journal of Biometrics,2011,3(3):228-245.
[4]张军光.电磁振动给料机的发展与控制方式探讨[J].城市建设理论研究,2014(11):201-201.ZHANG Jun-guang.Discussion on the Development and Control of Electromagnetic Vibrating Feeder[J].City Construction Theory Research,2014(11):201-201.
[5]骆有东.电磁振动给料器的性能及控制研究[J].包装工程,2005,26(4):54-55.LUO You-dong.Research on Performance and Control of Electromagnetic Vibration Feeder[J].Packaging Engineering,2005,26(4):54-55.
[6]郝晓琳,李志华.电磁振动给料机在配料称量系统中的应用分析[J].橡塑技术与装备,2005,31(10):41-44.HAO Xiao-lin,LI Zhi-hua.Application Analysis of Electromagnetic Vibrating Feeder in Batching Weighing System[J].Rubber Technology&Equipment,2005,31(10):41-44.
[7]顾平灿.电磁振动给料器给料速度的研究[J].机电工程,2012,29(7):790-794.GU Ping-can.Research on Feeding Velocity of Electromagnetic Vibrating Feeder[J].Mechanical Engineering,2012,29(7):790-794.
[8]万文,熊震江.电磁振动给料机的PID控制[J].矿山机械,2006(5):55-56.WAN Wen,XIONG Zhen-jiang.PID Control of Electromagnetic Vibrating Feeder[J].Mining Machinery,2006(5):55-56.
[9]ATTARAN S M,YUSOF R,SELAMAT H.A Novel Optimization Algorithm Based on Epsilon Constraint-RBF Neural Network for Tuning PID Controller in Fecoupled HVAC System[J].Applied Thermal Engineering,2016,99:613-624.
[10]周海涛,周建华,杭小宇.基于自适应模糊PID控制方法的研究[J].机械工程与自动化,2014(3):139-141.ZHOU Hai-tao,ZHOU Jian-hua,HANG Xiao-yu.Research on Adaptive Fuzzy PID Control Method[J].Mechanical Engineering&Automation,2014(3):139-141.
[11]杜汉强,张广文.基于模糊自适应PID控制器的张力控制系统[J].包装工程,2008,29(1):90-91.DU HAN-qiang,ZHANG Guang-wen.Tension Control System Based on Fuzzy Adaptive PID Controller[J].Journal of Packaging Engineering,2008,29(1):90-91.
[12]于瀛,杨世忠,衡丽帆.遗传模拟退火整定PID算法的应用仿真研究[J].自动化应用,2018(3):70-72.YU Ying,YANG Shi-zhong,HENG Li-fan.Application Simulation of Genetic Simulated Annealing PIDAlgorithm[J].Automation,2018(3):70-72.
[13]ZHANG M G,LI W H,LIU M Q.Adaptive PID Control Strategy Based on RBF Neural Network Identification[C]//International Conference on Neural Networks and Brain,Icnn&b IEEE,2005:1854-1857.
[14]申超群,宰守香.基于RBF-PID的啤酒灌装机贮液缸内液位控制[J].包装工程,2017,38(5):45-48.SHEN Chao-qun,ZAI Shou-xiang.Liquid Level Control in Liquid Storage Tank of Beer Filling Machine Based on RBF-PID[J].Packaging Engineering,2017,38(5):45-48.
[15]LIN F J,HSIEH H J,CHOU P H,et al.Digital Signal Processor-based Cross-coupled Synchronous Control of Dual Linear Motors via Functional Link Radial Basis Function Network[J].Iet Control Theory&Applications,2012,5(4):552-564.