直流電動機(jī)調(diào)速外文翻譯

1、畢業(yè)設(shè)計（論文）外文翻譯題 目 直流電動機(jī)電流、轉(zhuǎn)速雙閉環(huán)控制系統(tǒng)設(shè)計 專 業(yè) 電氣工程與自動化 班 級 2011級1班 學(xué) 生 夏于洋 指導(dǎo)教師 杜軍 重慶交通大學(xué) 2015無刷直流電動機(jī)調(diào)速的魯棒控制策略摘要：無刷直流電機(jī)（BLDCM）的速度伺服系統(tǒng)是多變量,具有非線性和強(qiáng)耦合性。齒槽轉(zhuǎn)矩和負(fù)荷的參數(shù)變化，擾動容易影響其無刷直流電機(jī)的性能。因此它是難以使用常規(guī)的PID控制來實(shí)現(xiàn)優(yōu)異的控制。為了解決執(zhí)行時所出現(xiàn)的不足之處 ，本文采用基于能夠自抗擾的控制BP神經(jīng)網(wǎng)絡(luò)活性算法來對無刷直流電機(jī)進(jìn)行控制。自抗干擾控制不依賴于精確的系統(tǒng)和它的擴(kuò)展。狀態(tài)觀測器可以準(zhǔn)確地估計該系統(tǒng)的擾動。然而，非線性反饋

2、的自抗擾的參數(shù)是很難獲得的.因此在這本文中，這些自抗擾的參數(shù)是通過BP神經(jīng)網(wǎng)絡(luò)在線自整定。仿真和實(shí)驗(yàn)結(jié)果表明，基于BP神經(jīng)網(wǎng)絡(luò)的自抗擾控制器可以提高在迅速伺服系統(tǒng)的性能，控制精度，適應(yīng)性和魯棒性。關(guān)鍵字：無刷直流電機(jī); BP神經(jīng)網(wǎng)絡(luò);自抗擾控制器;參數(shù)自整。I1 引言由于無刷直流電機(jī)的性能具有時變非線性，強(qiáng)耦合等特點(diǎn)，因此調(diào)速的高性能方法一直是一個重要的研究方向。PID是一種常見的控制方法。然而，它不能獲得預(yù)期的結(jié)果，以非線性對象的復(fù)雜任務(wù)和準(zhǔn)確的目標(biāo)這些利用PID控制就不能夠達(dá)到良好的控制目的。近年來，有關(guān)許多調(diào)速新的控制方法已經(jīng)出現(xiàn)在這些領(lǐng)域。比如：自適應(yīng)控制.卡爾曼過濾變結(jié)構(gòu)控制。模糊控

3、制，神經(jīng)網(wǎng)絡(luò)控制等等。自從自抗擾控制理論（ ADRC ）被曾經(jīng)擔(dān)任中國院士韓教授提出來的這些年里面，它是一個簡單而實(shí)用的方法。這種方法不依賴于控制目標(biāo)。它精確的數(shù)學(xué)模型可以估算和補(bǔ)償所有內(nèi)部和外部干擾的影響。當(dāng)系統(tǒng)建立起來以后.其控制的實(shí)時算法簡單，魯棒性強(qiáng)，具有快速的系統(tǒng)響應(yīng)和高抗干擾能力.到目前為止，這種方法仍然具有效率高，抗干擾能力強(qiáng)的優(yōu)勢，已被應(yīng)用到一些前沿科學(xué)和技術(shù)上。這些領(lǐng)域包括機(jī)器人，衛(wèi)星姿態(tài)控制，導(dǎo)彈飛行控制，坦克的火控和慣性導(dǎo)航等。不過，自抗擾控制器的參數(shù)需要在參數(shù)自整這些場合下才能進(jìn)行，因此這項(xiàng)研究設(shè)置在海內(nèi)外只處于探索階段。神經(jīng)網(wǎng)絡(luò)具有接近任何非線性函數(shù)的能力，還具備其結(jié)

4、構(gòu)和學(xué)習(xí)算法是簡單明了，因此神經(jīng)網(wǎng)絡(luò)是不依賴于控制對象的模式 。在本文中，通過自我學(xué)習(xí)的神經(jīng)網(wǎng)絡(luò)，自抗擾控制非線性反饋的參數(shù)可以在一個特定的最優(yōu)控制律里找到。仿真結(jié)果表明，基于BP神經(jīng)網(wǎng)絡(luò)的自抗擾控制器可以提高伺服系統(tǒng)的性能，在響應(yīng)速度，控制精度，適應(yīng)性和魯棒性這些方面的性能都能夠得到顯著的提高。2 無刷直流電動機(jī)的數(shù)學(xué)模型無刷直流電機(jī)產(chǎn)生的梯形反電動勢和施加的電流波形都是矩形波.其中自感為L，互感為M。因此，三相定子電壓平衡方程可以由以下狀態(tài)方程來表示：式中，,分別代表三相繞組a,b,c的相電壓. ,分別代表三相繞組a,b,c的相電流；,代表a,b,c三相相位的反電動勢；代表微分算子。無刷直

5、流電機(jī)的電磁轉(zhuǎn)矩由在定子繞組的電流和磁場在轉(zhuǎn)子磁鐵的相互作用下產(chǎn)生。該電磁轉(zhuǎn)矩方程：式中， 代表極數(shù)； 代表總導(dǎo)體數(shù);代表電機(jī)的機(jī)械角速度。3 控制方案如圖1所示，一個雙閉環(huán)控制與級聯(lián)連接相結(jié)合的控制系統(tǒng)中，內(nèi)環(huán)是電流環(huán)路，達(dá)到限制電流并確保伺服系統(tǒng)的電流。外環(huán)被設(shè)計來提高無刷直流電機(jī)的伺服系統(tǒng)的靜態(tài)和動態(tài)性能的穩(wěn)定性。速度環(huán)的輸出輸送給首端作為電流回路的設(shè)定電流信號。在本文中，速度環(huán)采用基于BP神經(jīng)網(wǎng)絡(luò)算法的自抗擾控制器，基于神經(jīng)網(wǎng)絡(luò)的自抗干擾控制系統(tǒng)的結(jié)構(gòu)如圖2所示。3.1有源抗擾控制自抗擾控制器主要由三個部分組成： “轉(zhuǎn)型過程安排” 。 “非線性反饋”和“擴(kuò)展?fàn)顟B(tài)觀察”。圖1無刷直流電動

6、機(jī)調(diào)速系統(tǒng)的原理圖圖2基于BP神經(jīng)網(wǎng)絡(luò)的自抗擾控制器的原理圖（1 ）轉(zhuǎn)型過程安排式中， 為控制目標(biāo)； 為的軌道信號；是一個時間最優(yōu)集成功能，其詳細(xì)方程表達(dá)式如方程（1）所示：（2）擴(kuò)展?fàn)顟B(tài)觀察式中， 代表控制周期。（3）非線性反饋.式中的參數(shù)可以在文獻(xiàn)中其他地方找到。3.2 BP神經(jīng)網(wǎng)絡(luò)的參數(shù)設(shè)定自抗擾控制器的自整參數(shù)可以使用BP神經(jīng)網(wǎng)絡(luò)建立,其中 ，,三個參數(shù)是由非線性反饋所產(chǎn)生的。 神經(jīng)網(wǎng)絡(luò),根據(jù)系統(tǒng)運(yùn)行狀態(tài),調(diào)整控制器參數(shù)達(dá)到一定的最佳化性能.神經(jīng)網(wǎng)絡(luò)的輸出對應(yīng)于控制器的三個可調(diào)參數(shù)，,對系統(tǒng)的內(nèi)部擾動,通過自主學(xué)習(xí)的神經(jīng)網(wǎng)絡(luò),與調(diào)整的權(quán)衡系數(shù)匹配,使一些神經(jīng)網(wǎng)絡(luò)輸出對應(yīng)于最優(yōu)控制下的參

7、數(shù)。 三層BP神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)設(shè)計,如圖3所示圖3 BP神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)圖圖中的rin(k)和yout(k)分別代表速度指令和速度反饋。輸入層的輸入公式：式中，M取決于輸入的數(shù)字，本文中它被設(shè)置為2 。它們是速度指令和速度反饋。輸入與輸出的公式式中，w代表隱含層，上級數(shù)的加權(quán)系數(shù)是與輸入，輸出和隱藏層相關(guān)。在論文中，隱藏層的節(jié)點(diǎn)被設(shè)置為3 。隱藏層神經(jīng)元的激活函數(shù)使用具有正和負(fù)特性的對稱S形函數(shù)。輸入層與輸出層的關(guān)系輸出層的輸出節(jié)點(diǎn)是三個可調(diào)參數(shù)，,，輸出層神經(jīng)元的激活函數(shù)使用具有正特性的S形函數(shù)。性能指標(biāo)函數(shù)按照梯度下降法修正權(quán)函數(shù)的網(wǎng)絡(luò)功能。通過加權(quán)梯度方向搜索函數(shù)的負(fù)系數(shù)，并添加一個使慣性項(xiàng)全

8、球最低的搜索快速收斂。其中是學(xué)習(xí)速率，本文中設(shè)置為0.3 ，系數(shù)設(shè)定到0.8。輸出層的學(xué)習(xí)算法4 仿真和實(shí)驗(yàn)結(jié)果在本文中，無刷直流電機(jī)伺服系統(tǒng)的仿真模型建立在Matlab / Simulink環(huán)境下。用于無刷直流電動機(jī)的實(shí)際參數(shù)可采取參考用于仿真的數(shù)據(jù)，如表1中所示表1電機(jī)參數(shù)額定轉(zhuǎn)速（r/min）電動勢系數(shù)（V/(rad/s)）繞線電阻（）自感（mH）互感(mH)轉(zhuǎn)動慣量30000.11412.10.72.5*10-54.1 系統(tǒng)的速度仿真當(dāng)系統(tǒng)沒有負(fù)載，給定的速度是3000轉(zhuǎn)/分（額定運(yùn)行狀態(tài)）。利用3種控制方法進(jìn)行模擬，該仿真結(jié)果示于圖4 。結(jié)果表明，基于BP神經(jīng)網(wǎng)絡(luò)系統(tǒng)的自抗擾控制器具

9、有最快的性能，并且系統(tǒng)沒有超調(diào)。圖4 在額定運(yùn)行情況下仿真曲線統(tǒng)計圖4.2 針對負(fù)載擾動系統(tǒng)的穩(wěn)定性模擬當(dāng)負(fù)載在時間0.07秒突然改變至0.25牛頓米，速度曲線如圖5所示。仿真結(jié)果表明，基于BP神經(jīng)網(wǎng)絡(luò)系統(tǒng)的自抗擾控制器具有最高的穩(wěn)定性。（a） 采用三中方法對宏觀轉(zhuǎn)速曲線仿真圖（b） 對微觀外部干擾的動態(tài)速度曲線圖圖5負(fù)荷變化的速度響應(yīng)曲線圖4.3 實(shí)驗(yàn)結(jié)果基于DSP和FPGA的新型硬件結(jié)構(gòu)如圖6所示。該控制器的硬件架構(gòu)是基于TMS320VC33 DSP和CYCLONE II FPCA 。 TMS320VC33是一種高性能的DSP與32一位浮點(diǎn)， 17 ns指令周期時間和每秒1.2億次浮點(diǎn)運(yùn)算

10、。 TMS320VC33既支持C語言，有支持匯編語言編程。它可以容易的進(jìn)行復(fù)雜計算。 CYCLONEII FPGA是基于V.90的1.2nm SRAM過程與密度超過64 K的邏輯元件，最高可以達(dá)到嵌入式RAM 1.1兆比特和嵌入式18乘法器。因?yàn)橛辛诉@個功能，它可以支持高性能DSP應(yīng)用。圖6實(shí)驗(yàn)平臺在實(shí)驗(yàn)中，一個恒定的速度3000r/min（額定運(yùn)行狀態(tài)） ，從開始到10ms的這段時間中。該實(shí)驗(yàn)的結(jié)果如圖7所示，實(shí)驗(yàn)結(jié)果表明，基于神經(jīng)網(wǎng)絡(luò)系統(tǒng)中的自抗擾控制器具有最快的性能時，系統(tǒng)沒有過沖?；魻杺鞲衅鳙@得的無電刷直流電動機(jī)，其控制系統(tǒng)的速度信號是由兩個環(huán)決定的：速度環(huán)和電流環(huán)。位置速度控制系統(tǒng)作

11、為外回路，并且電流環(huán)充當(dāng)?shù)膬?nèi)環(huán)控制系統(tǒng)?？刂品桨冈谒俣拳h(huán)實(shí)現(xiàn)。圖7實(shí)驗(yàn)結(jié)果5 總結(jié)本文提出了一種直流電機(jī)的動力學(xué)模型，提出了一種新的控制方案，根據(jù)這一模型運(yùn)算法則中可實(shí)用性。直流電動機(jī)應(yīng)用到該系統(tǒng)，具有很強(qiáng)的魯棒性。同時，一種新的基于現(xiàn)場可編程門陣列電機(jī)控制系統(tǒng)的硬件結(jié)構(gòu)（FPGA）和數(shù)字信號處理器（DSP）實(shí)現(xiàn)了所提出的算法。仿真和實(shí)驗(yàn)結(jié)果驗(yàn)證所提出的控制方案可以減輕干擾的影響，使系統(tǒng)的不確定性急劇下降。此外，對于靜態(tài)和動態(tài)性能的干擾控制具有較強(qiáng)的魯棒性，使系統(tǒng)的魯棒性大大的提高。來源：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