模糊神经网络PID算法在输油泵系统中的控制研究
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摘要
针对输油泵在复杂地形中,由外界的温度、湿度、原油特性等不良因素导致的漂离问题,本文研究与设计了模糊神经网络PID控制算法来对输油泵控制系统进行控制,结合常规PID控制,模糊控制,神经网络控制的优点,分析了输油泵控制系统的组成结构,设计了模糊神经网络PID控制算法的模型结构和实现过程。仿真实验结果表明,文中算法相比较常规PID控制算法,超调量减少约20%左右,相较于模糊PID超调量减少了约9%,达到稳定状态所需的控制时间也远低于常规PID和模糊PID,并且文中算法具有较好的自适应能力和抗干扰能力。
In complex terrain,external temperature,humidity,crude oil characteristics and other adverse factors will cause oil pump control systems to drift apart.To solve this problem,a fuzzy neural network PID algorithm is proposed in this paper to control the oil pump systems. Combined with the advantages of conventional PID control,fuzzy control and neural network control,and with the the composition and structure of an oil pump control system,the model of the fuzzy neural network PID control algorithm is devised and then implemented.The simulation results show that compared with the conventional PID and the fuzzy PID control algorithms, the devised algorithm can reduce the overshoot by about 20% and by about 9%,respectively,and reduce greatly the control time needed for achieving stability.And the proposed algorithm has good adaptability and anti-interference ability.
In complex terrain,external temperature,humidity,crude oil characteristics and other adverse factors will cause oil pump control systems to drift apart.To solve this problem,a fuzzy neural network PID algorithm is proposed in this paper to control the oil pump systems. Combined with the advantages of conventional PID control,fuzzy control and neural network control,and with the the composition and structure of an oil pump control system,the model of the fuzzy neural network PID control algorithm is devised and then implemented.The simulation results show that compared with the conventional PID and the fuzzy PID control algorithms, the devised algorithm can reduce the overshoot by about 20% and by about 9%,respectively,and reduce greatly the control time needed for achieving stability.And the proposed algorithm has good adaptability and anti-interference ability.
引文
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[10] JIA C Y,BAI T,SHAN X Y,et al.Cloud Neural Fuzzy PID Hybrid Integrated Algorithm of Flatness Control[J].Journal of Iron and Steel Research,2014,21(6):562.
[2] 鲍建成.永磁同步电机PID参数优化研究[J].计算机仿真,2012,29(4):247.BAO Jianchen.Research on Parameters Optimization of PMSM Speed Servo System[J].Computer Simulation,2012,29(4):247.(in Chinese)
[3] 罗映红,徐志奇,刘丽媛,等.模糊PID控制在交流伺服电机系统中的仿真研究[J].煤矿机械,2012,33(9):96.LUO Yinghong,XU Zhiqi,LIU Liyuan,et al.Simulation of Fuzzy PID Controller in AC Servo-motor System[J].Coal Mine Machinery,2012,33(9):96.(in Chinese)
[4] 李一波,宋述锡.基于模糊自整定PID四旋翼无人机悬停控制[J].控制工程,2013,20(5):910.LI Yibo,SONG Shuxi.Hovering Control for Quadrotor Unmanned Helicopter Based on Fuzzy Self-tuning PID Algorithm[J].Control Engineering of China,2013,20(5):910.(in Chinese)
[5] 王天辉,李阳,王峰.基于模糊神经网络的伺服系统PID控制[J].火力与指挥控制,2014,39(4):154.WANG Tianhui,LI Yang,WANG Feng.Study of PID Controller to Servo System Based on Fuzzy-Neural Network[J].Fire Control and Command Control,2014,39(4):154.(in Chinese)
[6] 许英杰,潘廷哲,胡志男,等.四层结构的模糊神经网络电梯群控算法研究及程序实现[J].计算机与数字工程,2017,45(9):1760.XU Yingjie,PAN Tingzhe,HU Zhinan,et al.Elevator Group Control Algorithm and Its Control Program Implementation Based on Fuzzy Neural Network with Four-layer Structure[J].Computer & Digital Engineering,2017,45(9):1760.(in Chinese)
[7] 和江,彭舒钰,侯文.模糊神经网络PID在数字舵机控制中的应用[J].计算机测量与控制,2016,24(10):80.HE Jiang,PENG Shuyu,HOU Wen.Application of Fuzzy Neural Network PID in Digital Servo Control[J].Computer Measurement & Control,2016,24(10):80.(in Chinese)
[8] 欧国徽,刘春波,潘丰.基于改进模糊神经网络的PID参数自整定算法[J].江南大学学报(自然科学报),2011,10(2):146.OU Guohui,LIU Chunbo,PAN feng.Self-Turing of PID Pararmeter Algorithm Based on Improved Fuzzy Neural Networks[J].Journal of Jiangnan University(Natural Science Edition),2011,10(2):146.(in Chinese)
[9] 郭瑞,贾振国.模糊神经网络PID在变压器风冷控制系统中的应用[J].长春工程学院学报(自然科学版),2017,18(3):51.GUO Rui,JIA Zhenguo.Application of Fuzzy Neural Network PID in Transformer Air Cooling Control System[J].Journal of Changchun Institute of Technology(Nature Science Edition),2017,18(3):51.(in Chinese)
[10] JIA C Y,BAI T,SHAN X Y,et al.Cloud Neural Fuzzy PID Hybrid Integrated Algorithm of Flatness Control[J].Journal of Iron and Steel Research,2014,21(6):562.