RBF整定PID在永磁同步电机控制中的应用研究
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摘要
电动汽车驱动电机具有非线性、时变性和耦合性等特性。为解决常规比例积分微分(PID)控制在电动汽车上下坡或负载发生变化等情况下控制效果不理想的问题,对电动汽车中永磁同步电机的驱动控制系统进行了研究。基于径向基函数(RBF)神经网络在电机控制领域的应用现状,利用RBF神经网络在线整定PID控制参数的方法,改善控制器对电动汽车驱动电机的控制效果。分析了永磁同步电机在电动汽车中的应用特性,建立了电动汽车中永磁同步电机的离散数学模型。通过Matlab仿真,验证了RBF神经网络在永磁同步电机等非线性系统中应用的可行性,并分别在RBF在线整定PID控制和常规PID控制两种情况下进行电机的调速控制仿真试验。试验结果表明,相比常规PID控制,RBF在线整定PID控制具有更好的实时性、抗干扰能力以及自适应能力,能够有效提高电动汽车行驶的稳定性。
The drive motor of electric vehicle has the characteristics of nonlinearity,time variation,coupling and etc. In order to solve the problem that the control effect of conventional proportion integral derivative( PID) control is not ideal when the electric vehicle is running uphill and downhill or under the load changes,the driving control system of permanent magnet synchronous motor( PMSM) in electric vehicle is studied. Based on the application of radical basis function( RBF) neural network in the field of motor control,the method of on-line tuning PID control parameters by RBF neural network is used to improve the control effect of the controller of drive motor. The application characteristics of permanent magnet synchronous motor in electric vehicle are analyzed. The discrete mathematical model of permanent magnet synchronous motor in electric vehicle is established. The feasibility of applying RBF neural network to permanent magnet synchronous motor and other non-linear systems is verified by the simulation test in Matlab. The RBF on-line tuning PID control and conventional PID control are respectively used. Simulation tests of motor speed control are carried out under different conditions. The experimental results show that the RBF on-line tuning PID control has better real-time performance,anti-interference ability and adaptive ability than those of the conventional PID control,and can effectively improve the driving stability of electric vehicles.
The drive motor of electric vehicle has the characteristics of nonlinearity,time variation,coupling and etc. In order to solve the problem that the control effect of conventional proportion integral derivative( PID) control is not ideal when the electric vehicle is running uphill and downhill or under the load changes,the driving control system of permanent magnet synchronous motor( PMSM) in electric vehicle is studied. Based on the application of radical basis function( RBF) neural network in the field of motor control,the method of on-line tuning PID control parameters by RBF neural network is used to improve the control effect of the controller of drive motor. The application characteristics of permanent magnet synchronous motor in electric vehicle are analyzed. The discrete mathematical model of permanent magnet synchronous motor in electric vehicle is established. The feasibility of applying RBF neural network to permanent magnet synchronous motor and other non-linear systems is verified by the simulation test in Matlab. The RBF on-line tuning PID control and conventional PID control are respectively used. Simulation tests of motor speed control are carried out under different conditions. The experimental results show that the RBF on-line tuning PID control has better real-time performance,anti-interference ability and adaptive ability than those of the conventional PID control,and can effectively improve the driving stability of electric vehicles.
引文
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[3]曹茂俊,李盼池,肖红.量子神经网络在PID参数调整中的应用[J].计算机工程,2011,37(12):182-184.
[4]李小彤.永磁同步电机矢量控制策咯的研究与实现[D].济南:济南大学,2017.
[5]赵钢,成丁雨.电动汽车永磁同步电机工况仿真分析[J].自动化仪表,2016,37(2):5-8.
[6]李昊洋,邱鑫,李思祥,等.永磁同步电机直接转矩矢量控制运行稳定性对比研究[J].南京师范大学学报(工程技术版),2018,18(1):30-36.
[7]曹文霞. RBF神经网络整定PID控制直线永磁同步电机的研究[D].合肥:合肥工业大学,2009.
[8]伍育红.聚类算法综述[J].计算机科学,2015,42(6A):491-499.
[9]强勇,凌有铸,贾冕茜.基于RBF神经网络的永磁同步电机速度控制[J].微电机,2013,32(8):53-56.
[10]刘金琨.先进PID控制及其Matlab仿真[M].北京:电子工业出版社,2003.
[11]朱卫云,付东翔.基于RBF神经网络的永磁伺服电机控制系统[J].电子科技,2016,43(1):161-168.
[12]王雪丽.基于RBF神经网络电液恒功率调速自整定PID控制[J].机床与液压,2016,44(22):115-117.