直流电动机调速外文翻译

1、直流电动机调速外文翻译毕业设计（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差重庆交通大学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，

2、非线性反馈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

3、变结构控制。模糊控制，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

4、非线性函数的能力，还具备其结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'R 0 00 R 0、0 0 R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：000 L-M 0、00 L-M)式中，露，4,生分别代表三相绕

5、组a,b, c的相电压.。，ib, i,.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：t 1 / . 4匕 NCO式中，P代表极数；N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

6、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“非线性反馈”和自抗扰控制器主要由三个部分组成：“转型过程安排” “扩展状态观察”。11图1无刷直流电动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fh = fhare.vrh）Vj = Vj = /z - v2是一个时间最优集v2 = v2 + h - fh式中，Z为控制目标；为为之的轨道信号；成功能, 其详细方程表达式如方程(1)所示：d = r-h+（%/）2通心)，卜|>4)%例"4flian=r-sign（a）a > dr-Sa<d(2)扩展状态观察e = Z

7、-y号=4+小（7一月巡）Z2 = Q + a,（一 42 即（%0.5,5）+瓦）式中，力代表控制周期。 (3)非线性反馈.G =%一ae, = 一 z, "O =力，I %/伍必为）+力,2/。2，j）+力,3 %/（6，。3,6）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，其中力凡3三个参数 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处,乩2,43,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

8、神经网络输出对应于最优控制下的参数。 三层BP神经网络的结构设计,如图3所示output layerHidden layer (9 neurons)rin(k)上Input layer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：%)=4/)+与,/ = 12式中，M取决于输入的数字，本文中它被设置为2 o它们是速度指令和速度反馈。 输入与输出的公式M甲短>=0一) = /&伏)=(1,2,Q)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 o隐藏层神经元的激活函数使用具

9、有正和负特性的对称S形函数。/、 / x exp(¥)-exp(-x)f(x) = tanh(r) = _-exp(r) + expQx)输入层与输出层的关系Q/=0y产= &(*')' 犬=凰y；" (k) = %. 娟=%输出层的输出节点是三个可调参数见，%,外,输 出层神经元的激活函数使用具有正特性的S形函数。(x) = i(l + tanh(r) =exp(r)exp(r) + exp(-x)性能指标函数E(k) = (rin(k)- viit(k)y2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

10、全球最低的搜索快速收敛。其中77是学习速率,输出层的学习算法婿)(幻=-+必“:依-I)本文中设置为0.3 ,。系数设定到0.8。“：)*)= a"y,(k) + 必/2)(k -1) e=；2”)±¥ 我/ = 12 ,0 I)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(Q)自感(mH)互感(mH)转动惯量 kg"230000. 11412. 10.72

11、. 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 o结果表明，基于BP神经网络系统的自抗扰控制器具有最快的性能，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针对负教扰动系统的稳定性模拟,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 D

12、SP 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。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nm 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

13、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

14、较强的鲁棒 性，使系统的鲁棒性大大的提高。来源：Zhi Liu ,Bai Fen Llu.Robust Control Strategy for the Speed Control of Brushless DC Motor,2013Robust Control Strategy for the Speed Control of Brushless DCMotorAbstract ： Brushless DC motor(BLDCM)speed servo system is multivariable, nonlinearand strong coupling. The parameter

15、variation, the cogging torque and the load disturbance easily influence its performance. Therefore it is difficult to achieve superior perforin ance by using the conventional PID controller. To solve the deficiency, the paper represents the algorithm of active-disturbance rejection control(ADRC)base

16、d on back. Propagation (BP) neural network. The ADRC is independent on accurate system and its extended.state observer can estimate the disturbance of the system accuratelv.However, the parameters of Nonlinear Feedback(NF)in ADRC are difficult to obtain. So in this paper.these parameters are self-tu

17、rned by the BP neural network. The simulation and experiment results indicate that the ADRC based on BP neural network can improve the performances of the servo system in rapidity> control accuracy, adaptability and robustness.Keywords： brushless DC motor(BLDCM)； BP(back propagation algorithms)；

18、ADRC(active Disturbance rejection control)； parameters selfturning1 IntroductionAccording to the properties of BLDCM . Time-variation nonlinear and strong couple, the high performance method of speed regulation has been an essential research direction.PID is a common method. However, it cannot gain

19、the expected result to nonlinear object with the complicated mission and accurate goals daily. In recent years, many novel controlling methods of speed regulation have appeared in these fields： adaptive control.Kalman filter variable structure control fuzzy control,neural network control,etc.The the

20、ory of auto-disturbance rejection control(ADRC)proposed these years is an easy and practical scheme. It was invented by Prof. Han who once served in Chinese Academy of Sciences. This method does not rely on a precise mathematical model of controlled object. It can estimate and compensate the influen

21、ces of all internal and external disturbances inreal time when the system is activated. The control has the advantage of simple algorithm, strong robustness, fast system response and high anti-interference ability At present, this method has been applied to a number of fields of frontier science and

22、 technology, such as robotics, satellite attitude control, missile flight control, the fire control of tank and the inertia navigation. However, the parameters of ADRC need to be set in these occasions. The study of the parameters self-turning is only at an exploratory stage at home and abroad.BP ne

23、ural network has the capability of approaching to any nonlinear function, and its structure and learning algorithm is simple and clear , which is not dependent on the controlled object model.In this paper, through self-learning network, the nonlinear Feedback (NF) parameters in ADRC under a particul

24、ar optimal control law can be found The simulation results indicate that the ADRC based on BP neural network can improve the performances of the servo system in response speed, control accuracy, adaptability and robustness.2 Mathematical Model of the BLDCMThe BLDCM produces a trapezoidal back electr

25、o motive force (EMF). and the applied current waveform is rectangular-shaped. The self-inductance is L. and the mutual inductance is M. Hence the three-phase stator voltage balance equation can be expressed by the following state equation：0L-M0where ua, uh, uc are the phase voltage of three-phase wi

26、ndings. "3，" are the phase current of three-phase windings q , ，ec are the phase back EMF ； p is differential operator.The electromagnetic torque of BLDCM is generated by the interaction of the current in stator windings and the magnetic field in rotor magnet. The electromagnetic torque eq

27、uation iswhere P is pole numbers； N is total conductor numbers; co is mechanical angular velocity of motor.3 Proposed Control SchemeAs is shown in Fig. L a double looped control with cascade connection has been adopted in the system. The inner loop is current loop which limits theultimate current an

28、d ensures the stability of the servo system. The 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 the current loop.In this paper, the speed loop uses the algorithm of ADRC based on BP neural

29、 network (ADRC*ln Fig.l). The structure of ADRC based on BP neural network control system is shown in Fig. 2.4 . 1 Active-Disturbance Rejection ControlADRC controller consists of three main parts： “ Transition Process Arranged “ Nonlinear FeedbackandExtendedState Observer”9Fig. 1 Schematic of BLDCM

30、speed control systemFig. 2 Schematic of ADRC based on BP neural networkl)Transition Process Arranged.fh = fhmKe*/o，h)v=v=h-匕v2 = v2 + h - fhwhere v0 is the control objective； ” is the track signal of % ； fliaiK) is a time optimal integrated function, whose detailed expression is described as Eq. .d

31、= rhfl0=/2+10r-|y|4十 (城|y| > 4)r - sign(aa> dr' .a<a. d2)Extended一State Observer(ESO)c = 4 - y4 =4+力(£)Z2 = Z1 +h(-J32fal(e,Q.5,3)+b0u) where b is the control cycle.3)Output of Nonlinear Feedback(NF)<e2 = u2 - z2“0 = Po ,川(G0,。)+ g,2 , W0邛，%，5)+ 处 %/(6,6)where the parameters can

32、 be found in Ref.3. 2 Parameters Turned by BP Neural NetworkThe parameters self-turning ADRC can be established using the BP neural network. Three parameters 讥、,/3o2,poi in NF are made online.Neural network, according to the system running status,adjusts the controller parameters to achieve a certai

33、n performance optimization.lt glows the output of neural network corresponds to auto-disturbance rejection controller in the three adjustable parameters 为， 加， Through self-learning neural networks, with the weighed coefficient of adjustment, It makes some kind of neural network output correspond to

34、the parameters under the optimal control rate.Three-layer BP neural nebvork's structure is designed in this paper, as shown in Fig. 3.Hidden layer (9 neurons)Fig. 3 Structure of BP neural networkwhere rin(k)and yout(k)are speed command and the speed feedback.The inputs of the input layer arey? =

35、</)+%" = 12 ,Mwhere M depends on the numbers of the input which is set to 2 in this paper. They are the speed command and the speed feedback.The inputs and the outputs areMT(&)=£尾靖'+/六。)产") = /(,),i = (L2,0)where w are the weighted coefficients of the hidden layer. Upper nu

36、mbers are the input, output> and the hidden layer. In the paper, the node of the hidden layer is set to 3.The activation function of the hidden layer neuron uses the symmetric sigmoid function with positive and negative feature.一、.一、exp(r)-expx)f(x) = tanh(r)=exp(v) + exp(-x)The input and the out

37、put of the output layer areQi=0 幻) < 一(=为 一(%)= % . 娟伙)=%The output nodes of the output layer are three adjustable parameters 瓦、, 讥瓦、. The activation function of the output layer neuron uses the sigmoid function with positive feature./、1 /. /、 exp(r)gM = -0 + tanh(v)=2exp(r) + expGx)The performan

38、ce index function isE(k) =(女)一(女)2In accordance with the gradient descent method to amend the network function of the weight function. The negative coefficient of the function by a weighted gradient direction search, and add one to make the search fast convergence of the global minimum of the inerti

39、a term .*) = F 2 + 必靖也-1)where7Is the learning rate and set to 0. 3, and a is the coefficient and set to 0. 8. The learning algorithm of the output layer is(女)=的 y,也)+ 必可：( 一 1)3姆=f(限)Z4成(Q,i = l,2,0/-()4 Simulation and Experimental ResultsIn this paper > the simulation model of servo system for

40、brushless DC motor has been established in Matlab / Simulinke The actual parameters used for brushless DC motor can be taken reference for simulation ones, as shown in Table 1Table 1 Motor parametersRated spned (r/min)EMF efficient (V/(rad/s)Winding ft)Sclf-inducUincc( mH)Mutual ir)durtanrc(mH)Rotat

41、ion inertia (% " n?)WOO0. 11412. !0.72.5xl04. 1 Rapidity of the System Due to the SimulationWhen the system has no load, the simulation of three controlling methods is used. The given speed is 3000 r / min(the rated running state). The simulation results are shown in Fig. 4, The results show th

42、at the ADRC based on BP neural network system has the fastest performance when the system has no overshoot.> 3 Settling time: 0.01 & 330002 Seltling timr; 0.023 s200010001 SettliTig time: 0.037 si 1FID:ZADRC3-ADRC kw/ 产 BP I neural network00.020.040.060.0SFig. 4 Simulation curves in the rated

43、 running stat 4. 2 Stability of the System Against Load Disturbance Due to the SimulationWhen the load suddenly changes to 0. 25 N -m at time 0. 07 s, the velocity curves are shown in Fig. 5. The simulation results show that the ADRC based on BP neural network system has the highest stability.(a)Rot

44、ate speed curve when adopt three methodon macroscopic view(b) Dynamic speed curve due to external disturbance on microscopic viewFig. 5 Speed response curve due to variable loads4. 3 Experimental ResultsA novel hardware structure based on DSP and FPGA is given inFig, 6. Hardware architecture of this

45、 controller is based on TMS320VC33 DSP and CYCLONE II FPCA.TMS320VC33 is a high performance DSP with 32-bit floating一point, 17 ns instruction cycle time and 120 million floating-point operations per seconcL TMS320VC33 supports programming with both C language and assembly language. And it can carry out complex calculation easily. CYCLONEII FPGA is based on a 1. 2 V. 90 nm SRAM process with