基于BP神经网络模型的磁悬浮水泵PID参数优化
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摘要
为了设计动态性能更优的磁悬浮水泵磁轴承支撑系统,本文利用多功能磁悬浮试验平台,通过整定磁轴承PID控制参数实现了转轴的5自由度稳定悬浮,然后随机选取径向磁轴承B的Y方向进行辨识试验,建立了其控制系统被控对象的BP神经网络模型,并采用Simulink进行PID参数优化,最后模拟磁悬浮水泵3种不同工况进行控制仿真对比。使用优化参数的系统仿真结果显示系统响应更快,试验结果表明,0~4000 r/min慢扫频过程中,转子X、Y方向位移分别降低了32.9%和24.1%,试验结果为磁悬浮水泵的PID参数优化提供了一种方法。
To design better performance dynamic of magnetic bearing support system of magnetic suspension pump, the multifunction magnetic levitation experiment platform was used to realize the 5-DOF stability suspension of the rotating shaft by setting the PID control parameters of the magnetic bearing. Then,the Y-direction of the radial magnetic bearing B was randomly selected to identify the BP neural network model of the controlled object,and PID parameters were optimized by Simulink. Finally,the simulation and comparison under three different working conditions were carried out. The simulation results show that the response of the system with optimized parameters is faster. And the experimental results show that the rotor's displacement in X and Y direction are reduced by 32.9% and 24.1% respectively in the course of 0~4000 r/min. This article provides a novel method for optimizing the PID parameters of the magnetic suspension pump.
To design better performance dynamic of magnetic bearing support system of magnetic suspension pump, the multifunction magnetic levitation experiment platform was used to realize the 5-DOF stability suspension of the rotating shaft by setting the PID control parameters of the magnetic bearing. Then,the Y-direction of the radial magnetic bearing B was randomly selected to identify the BP neural network model of the controlled object,and PID parameters were optimized by Simulink. Finally,the simulation and comparison under three different working conditions were carried out. The simulation results show that the response of the system with optimized parameters is faster. And the experimental results show that the rotor's displacement in X and Y direction are reduced by 32.9% and 24.1% respectively in the course of 0~4000 r/min. This article provides a novel method for optimizing the PID parameters of the magnetic suspension pump.
引文
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[2]张晶.离心水泵隔振稳定性影响因素的研究[D].沈阳:沈阳工业大学,2014.
[3]杨勇,周韬宜,郝鹏.三蒸馏混合蜡油泵振动原因分析[J].流体机械,2016,44(4):56-58.
[4]白金刚,张小章,张剀,等.磁悬浮储能飞轮系统中的磁轴承参数辨识[J].清华大学学报(自然科学版),2008,48(3):382-385.
[5]Schweitzer G,Bleuler H,Traxler A.Active Magnetic Bearings-Basics,Properties and Application of Active Magnetic Bearings[M].ETH,Switzerland:Hochschulverlag AG,1994.
[6]胡业发,周祖德,江征风.磁力轴承的基础理论与应用[M].北京:机械工业出版社,2006.
[7]丁力,房建成,魏彤,等.一种抑制扰动的轴向磁轴承鲁棒控制新方法[J].北京航空航天大学学报,2010,36(4):420-423.
[8]魏彤,房建成.磁悬浮控制力矩陀螺磁轴承的变工作点线性化自适应控制方法[J].机械工程学报,2007,43(6):110-115.
[9]张菊秀,全书海,王永生.多模态智能控制在磁悬浮轴承系统中的应用[J].武汉理工大学学报(交通科学与工程版),2007,31(2):364-366.
[10]王勤.基于神经网络的磁悬浮轴承系统辨识和控制研究[D].武汉:武汉理工大学,2007.
[11]周瑾,倪佐僖.基于不平衡响应的磁悬浮轴承刚度阻尼辨识方法研究[J].振动与冲击,2013,32(3):29-34.
[12]沈钺,虞烈.主动磁轴承-转子系统的动态结构参数辨识[J].机床与液压,2001(6):7-8.
[13]瞿海妮.基于神经网络的磁悬浮轴承系统辨识研究[D].苏州:苏州大学,2006.
[14]萧德云.系统辨识理论及应用[M].北京:清华大学出版社,2014.
[15]侯媛彬,周莉,王立琦,等.系统辨识[M].西安:西安电子科技大学出版社,2014.
[16]罗兵,黄万杰,杨帅.基于Tan-Sigmoid函数参数调整的BP神经网络改进算法[J].重庆大学学报(自然科学版),2006,29(1):150-153.