直流电动机调速外文翻译

1、毕业设计（论文）外文翻译题 目 直流电动机电流、转速双闭环控制系统设计 专 业 电气工程与自动化 班 级 2011级1班 学 生 夏于洋 指导教师 杜军 重庆交通大学 2015无刷直流电动机调速的鲁棒控制策略摘要：无刷直流电机（BLDCM）的速度伺服系统是多变量,具有非线性和强耦合性。齿槽转矩和负荷的参数变化，扰动容易影响其无刷直流电机的性能。因此它是难以使用常规的PID控制来实现优异的控制。为了解决执行时所出现的不足之处 ，本文采用基于能够自抗扰的控制BP神经网络活性算法来对无刷直流电机进行控制。自抗干扰控制不依赖于精确的系统和它的扩展。状态观测器可以准确地估计该系统的扰动。然而，非线性反馈

2、的自抗扰的参数是很难获得的.因此在这本文中，这些自抗扰的参数是通过BP神经网络在线自整定。仿真和实验结果表明，基于BP神经网络的自抗扰控制器可以提高在迅速伺服系统的性能，控制精度，适应性和鲁棒性。关键字：无刷直流电机; BP神经网络;自抗扰控制器;参数自整。I1 引言由于无刷直流电机的性能具有时变非线性，强耦合等特点，因此调速的高性能方法一直是一个重要的研究方向。PID是一种常见的控制方法。然而，它不能获得预期的结果，以非线性对象的复杂任务和准确的目标这些利用PID控制就不能够达到良好的控制目的。近年来，有关许多调速新的控制方法已经出现在这些领域。比如：自适应控制.卡尔曼过滤变结构控制。模糊控

3、制，神经网络控制等等。自从自抗扰控制理论（ ADRC ）被曾经担任中国院士韩教授提出来的这些年里面，它是一个简单而实用的方法。这种方法不依赖于控制目标。它精确的数学模型可以估算和补偿所有内部和外部干扰的影响。当系统建立起来以后.其控制的实时算法简单，鲁棒性强，具有快速的系统响应和高抗干扰能力.到目前为止，这种方法仍然具有效率高，抗干扰能力强的优势，已被应用到一些前沿科学和技术上。这些领域包括机器人，卫星姿态控制，导弹飞行控制，坦克的火控和惯性导航等。不过，自抗扰控制器的参数需要在参数自整这些场合下才能进行，因此这项研究设置在海内外只处于探索阶段。神经网络具有接近任何非线性函数的能力，还具备其结

4、构和学习算法是简单明了，因此神经网络是不依赖于控制对象的模式 。在本文中，通过自我学习的神经网络，自抗扰控制非线性反馈的参数可以在一个特定的最优控制律里找到。仿真结果表明，基于BP神经网络的自抗扰控制器可以提高伺服系统的性能，在响应速度，控制精度，适应性和鲁棒性这些方面的性能都能够得到显著的提高。2 无刷直流电动机的数学模型无刷直流电机产生的梯形反电动势和施加的电流波形都是矩形波.其中自感为L，互感为M。因此，三相定子电压平衡方程可以由以下状态方程来表示：式中，,分别代表三相绕组a,b,c的相电压. ,分别代表三相绕组a,b,c的相电流；,代表a,b,c三相相位的反电动势；代表微分算子。无刷直

5、流电机的电磁转矩由在定子绕组的电流和磁场在转子磁铁的相互作用下产生。该电磁转矩方程：式中， 代表极数； 代表总导体数;代表电机的机械角速度。3 控制方案如图1所示，一个双闭环控制与级联连接相结合的控制系统中，内环是电流环路，达到限制电流并确保伺服系统的电流。外环被设计来提高无刷直流电机的伺服系统的静态和动态性能的稳定性。速度环的输出输送给首端作为电流回路的设定电流信号。在本文中，速度环采用基于BP神经网络算法的自抗扰控制器，基于神经网络的自抗干扰控制系统的结构如图2所示。3.1有源抗扰控制自抗扰控制器主要由三个部分组成： “转型过程安排” 。 “非线性反馈”和“扩展状态观察”。图1无刷直流电动

6、机调速系统的原理图图2基于BP神经网络的自抗扰控制器的原理图（1 ）转型过程安排式中， 为控制目标； 为的轨道信号；是一个时间最优集成功能，其详细方程表达式如方程（1）所示：（2）扩展状态观察式中， 代表控制周期。（3）非线性反馈.式中的参数可以在文献中其他地方找到。3.2 BP神经网络的参数设定自抗扰控制器的自整参数可以使用BP神经网络建立,其中 ，,三个参数是由非线性反馈所产生的。 神经网络,根据系统运行状态,调整控制器参数达到一定的最佳化性能.神经网络的输出对应于控制器的三个可调参数，,对系统的内部扰动,通过自主学习的神经网络,与调整的权衡系数匹配,使一些神经网络输出对应于最优控制下的参

7、数。 三层BP神经网络的结构设计,如图3所示图3 BP神经网络结构图图中的rin(k)和yout(k)分别代表速度指令和速度反馈。输入层的输入公式：式中，M取决于输入的数字，本文中它被设置为2 。它们是速度指令和速度反馈。输入与输出的公式式中，w代表隐含层，上级数的加权系数是与输入，输出和隐藏层相关。在论文中，隐藏层的节点被设置为3 。隐藏层神经元的激活函数使用具有正和负特性的对称S形函数。输入层与输出层的关系输出层的输出节点是三个可调参数，,，输出层神经元的激活函数使用具有正特性的S形函数。性能指标函数按照梯度下降法修正权函数的网络功能。通过加权梯度方向搜索函数的负系数，并添加一个使惯性项全

8、球最低的搜索快速收敛。其中是学习速率，本文中设置为0.3 ，系数设定到0.8。输出层的学习算法4 仿真和实验结果在本文中，无刷直流电机伺服系统的仿真模型建立在Matlab / Simulink环境下。用于无刷直流电动机的实际参数可采取参考用于仿真的数据，如表1中所示表1电机参数额定转速（r/min）电动势系数（V/(rad/s)）绕线电阻（）自感（mH）互感(mH)转动惯量30000.11412.10.72.5*10-54.1 系统的速度仿真当系统没有负载，给定的速度是3000转/分（额定运行状态）。利用3种控制方法进行模拟，该仿真结果示于图4 。结果表明，基于BP神经网络系统的自抗扰控制器具

9、有最快的性能，并且系统没有超调。图4 在额定运行情况下仿真曲线统计图4.2 针对负载扰动系统的稳定性模拟当负载在时间0.07秒突然改变至0.25牛顿米，速度曲线如图5所示。仿真结果表明，基于BP神经网络系统的自抗扰控制器具有最高的稳定性。（a） 采用三中方法对宏观转速曲线仿真图（b） 对微观外部干扰的动态速度曲线图图5负荷变化的速度响应曲线图4.3 实验结果基于DSP和FPGA的新型硬件结构如图6所示。该控制器的硬件架构是基于TMS320VC33 DSP和CYCLONE II FPCA 。 TMS320VC33是一种高性能的DSP与32一位浮点， 17 ns指令周期时间和每秒1.2亿次浮点运算

10、。 TMS320VC33既支持C语言，有支持汇编语言编程。它可以容易的进行复杂计算。 CYCLONEII FPGA是基于V.90的1.2nm SRAM过程与密度超过64 K的逻辑元件，最高可以达到嵌入式RAM 1.1兆比特和嵌入式18乘法器。因为有了这个功能，它可以支持高性能DSP应用。图6实验平台在实验中，一个恒定的速度3000r/min（额定运行状态） ，从开始到10ms的这段时间中。该实验的结果如图7所示，实验结果表明，基于神经网络系统中的自抗扰控制器具有最快的性能时，系统没有过冲。霍尔传感器获得的无电刷直流电动机，其控制系统的速度信号是由两个环决定的：速度环和电流环。位置速度控制系统作

11、为外回路，并且电流环充当的内环控制系统。控制方案在速度环实现。图7实验结果5 总结本文提出了一种直流电机的动力学模型，提出了一种新的控制方案，根据这一模型运算法则中可实用性。直流电动机应用到该系统，具有很强的鲁棒性。同时，一种新的基于现场可编程门阵列电机控制系统的硬件结构（FPGA）和数字信号处理器（DSP）实现了所提出的算法。仿真和实验结果验证所提出的控制方案可以减轻干扰的影响，使系统的不确定性急剧下降。此外，对于静态和动态性能的干扰控制具有较强的鲁棒性，使系统的鲁棒性大大的提高。来源：Zhi Liu ,Bai Fen Liu.Robust Control Strategy for the

12、Speed Control of Brushless DC Motor,20138Robust Control Strategy for the Speed Control of Brushless DC MotorAbstract：Brushless DC motor(BLDCM)speed servo system is multivariablenonlinear and strong couplingThe parameter variationthe cogging torque and the load disturbance easily influence its perfor

13、manceTherefore it is difficult to achieve superior perform ance by using the conventional PID controllerTo solve the deficiency，the paper represents the algorithm of active-disturbance rejection control(ADRC)based on backPropagation (BP) neural networkThe ADRC is independent on accurate system and i

14、ts extendedstate observer can estimate the disturbance of the system accurately.However，the parameters of Nonlinear Feedback(NF)in ADRC are difficult to obtainSo in this paperthese parameters are self-turned by the BP neural networkThe simulation and experiment results indicate that the ADRC based o

15、n BP neural network can improve the performances of the servo system in rapidity，control accuracy，adaptability and robustnessKeywords：brushless DC motor(BLDCM)；BP(back propagation algorithms)；ADRC(active Disturbance rejection contro1)；parameters selfturning1 IntroductionAccording to the properties o

16、f BLDCM Time-variation nonlinear and strong couple，the high performance method of speed regulation has been an essential research directionPID is a common methodHoweverit cannot gain the expected result to nonlinear object with the complicated mission and accurate goals daily In recent years， many n

17、ovel controlling methods of speed regulation have appeared in these fields：adaptive control .Kalman filter variable structure control fuzzy control,neural network control,etcThe theory of auto-disturbance rejection control(ADRC)proposed these years is an easy and practical schemeIt was invented by P

18、rof Han who once served in Chinese Academy of Sciences This method does not rely on a precise mathematical model of controlled objectIt can estimate and compensate the influences of all internal and external disturbances inreal time when the system is activatedThe control has the advantage of simple

19、 algorithm,strong robustness，fast system response and high anti-interference ability At presentthis method has been applied to a number of fields of frontier science and technologysuch as robotics，satellite attitude contro1missile flight control， the fire control of tank and the inertia navigationHo

20、wever，the parameters of ADRC need to be set in these occasionsThe study of the parameters self-turning is only at an exploratory stage at home and abroadBP neural network has the capability of approaching to any nonlinear function，and its structure and learning algorithm is simple and clear ，which i

21、s not dependent on the controlled object mode1In this paper，through self-learning network，the nonlinear Feedback (NF) parameters in ADRC under a particular optimal control law can be found The simulation results indicate that the ADRC based on BP neural network can improve the performances of the se

22、rvo system in response speed， control accuracy， adaptability and robustness2 Mathematical Model of the BLDCMThe BLDCM produces a trapezoidal back electro motive force (EMF)and the applied current waveform is rectangularshapedThe self-inductance is Land the mutual inductance is M. Hence the three-pha

23、se stator voltage balance equation can be expressed by the following state equation：where , are the phase voltage of three-phase windings. ,are the phase current of threephase windings；,are the phase back EMF；is differential operatorThe electromagnetic torque of BLDCM is generated by the interaction

24、 of the current in stator windings and the magnetic field in rotor magnet The electromagnetic torque equation iswhere is pole numbers； is total conductor numbers; is mechanical angular velocity of motor3 Proposed Control SchemeAs is shown in Fig1，a double looped control with cascade connection has b

25、een adopted in the system The inner loop is current loop which limits theultimate current and ensures the stability of the servo systemThe outer loop is designed to improve the static and dynamic performances of the BLDCM servo system The output of speed loop is given as the set current signal of th

26、e current loopIn this paper，the speed loop uses the algorithm of ADRC based on BP neural network (ADRC*in Fig.1) The structure of ADRC based on BP neural network control system is shown in Fig231 Active-Disturbance Rejection ControlADRC controller consists of three main parts：“ Transition Process Ar

27、ranged ” “ Nonlinear Feedback”and“ExtendedState Observer”Fig1 Schematic of BLDCM speed control systemFig2 Schematic of ADRC based on BP neural network1) Transition Process Arrangedwhere is the control objective； is the track signal of；is a time optimal integrated function，whose detailed expression i

28、s described as Eq(1)2) ExtendedState Observer(ESO)where is the control cycle.3) Output of Nonlinear Feedback(NF)where the parameters can be found in Ref.32 Parameters Turned by BP Neural NetworkThe parameters self-turning ADRC can be established using the BP neural network. Three parameters，,in NF a

29、re made onlineNeural network，according to the system running status,adjusts the controller parameters to achieve a certain performance optimization.It glows the output of neural network corresponds to auto-disturbance rejection controller in the three adjustable parameters，,Through self-learning neu

30、ral networks，with the weighed coefficient of adjustment，it makes some kind of neural network output correspond to the parameters under the optimal control rateThree·layer BP neural network s structure is designed in this paper，as shown in Fig3Fig3 Structure of BP neural networkwhere rin(k)and y

31、out(k)are speed command and the speed feedbackThe inputs of the input layer arewhere M depends on the numbers of the input which is set to 2 in this paper They are the speed command and the speed feedbackThe inputs and the outputs arewhere w are the weighted coefficients of the hidden layerUpper num

32、bers are the input，output，and the hidden layer In the paper，the node of the hidden layer is set to 3The activation function of the hidden layer neuron uses the symmetric sigmoid function with positive and negative featureThe input and the output of the output layer areThe output nodes of the output

33、layer are three adjustable parameters，, The activation function of the output layer neuron uses the sigmoid function with positive featureThe performance index function isIn accordance with the gradient descent method to amend the network function of the weight functionThe negative coefficient of th

34、e function by a weighted gradient direction search， and add one to make the search fast convergence of the global minimum of the inertia term whereis the learning rate and set to 03，and is the coefficient and set to 08The learning algorithm of the output layer is4 Simulation and Experimental Results

35、In this paper， the simulation model of servo system for brushless DC motor has been established in MatlabSimulink The actual parameters used for brushless DC motor can be taken reference for simulation ones，as shown in Table 1Table 1 Motor parameters41 Rapidity of the System Due to the SimulationWhe

36、n the system has no load the simulation of three controlling methods is used. The given speed is 3000 rmin(the rated running state)The simulation results are shown in Fig4 The results show that the ADRC based on BP neural network system has the fastest performance when the system has no overshoot.Fi

37、g4 Simulation curves in the rated running stat42 Stability of the System Against Load Disturbance Due to the SimulationWhen the load suddenly changes to 025 N ·m at time 007 s，the velocity curves are shown in Fig5The simulation results show that the ADRC based on BP neural network system has th

38、e highest stability(a) Rotate speed curve when adopt three method on macroscopic view(b) Dynamic speed curve due to external disturbance on microscopic viewFig5 Speed response curve due to variable loads43 Experimental ResultsA novel hardware structure based on DSP and FPGA is given in Fig6 Hardware

39、 architecture of this controller is based on TMS320VC33 DSP and CYCL0NE II FPCA.TMS320VC33 is a high performance DSP with 32一bit floatingpoint， 17 ns instruction cycle time and 120 million floating-point operations per second TMS320VC33 supports programming with both C language and assembly language And it can carry out complex calculation easily. CYCL0NEII FPGA is based on a 12 V90 nm SRAM process with densities over 64 K logic elements,up to 11 Mbits of embedded RAM and embedded 18 multipliers. With this features, it supports high performance D