直流電動(dòng)機(jī)調(diào)速外文翻譯--無(wú)刷直流電動(dòng)機(jī)調(diào)速的魯棒控制策略

1、<p>　　英文1800單詞,中文2878字</p><p><b>　　畢業(yè)設(shè)計(jì)（論文）</b></p><p><b>　　外文翻譯</b></p><p>　　題 目 直流電動(dòng)機(jī)電流、轉(zhuǎn)速雙</p><p>　　閉環(huán)控制系統(tǒng)設(shè)計(jì) </p><p&

2、gt;　　專 業(yè) 電氣工程與自動(dòng)化 </p><p>　　班 級(jí) </p><p>　　學(xué) 生 </p><p>　　指導(dǎo)教師 </p><p><b>　　2015<

3、;/b></p><p>　　無(wú)刷直流電動(dòng)機(jī)調(diào)速的魯棒控制策略</p><p>　　Zhi Liu ,Bai Fen Liu</p><p>　　摘要：無(wú)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(dòng)容易影響其無(wú)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(lái)實(shí)現(xiàn)優(yōu)異的控制。為了解決執(zhí)行時(shí)所出現(xiàn)的不足之

4、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(lái)對(duì)無(wú)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(cè)器可以準(zhǔn)確地估計(jì)該系統(tǒng)的擾動(dòng)。然而，非線性反饋的自抗擾的參數(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(guò)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。</p><p>　　關(guān)鍵字：無(wú)刷直流電機(jī);

5、 BP神經(jīng)網(wǎng)絡(luò);自抗擾控制器;參數(shù)自整。</p><p><b>　　1 引言</b></p><p>　　由于無(wú)刷直流電機(jī)的性能具有時(shí)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(gè)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(duì)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(lái)，有關(guān)許多調(diào)速新的控制

6、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(guò)濾變結(jié)構(gòu)控制。模糊控制，神經(jīng)網(wǎng)絡(luò)控制等等。</p><p>　　自從自抗擾控制理論（ ADRC ）被曾經(jīng)擔(dān)任中國(guó)院士韓教授提出來(lái)的這些年里面，它是一個(gè)簡(jiǎn)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(lái)以后.其控制的實(shí)時(shí)算法簡(jiǎn)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

7、率高，抗干擾能力強(qiáng)的優(yōu)勢(shì)，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(guò)，自抗擾控制器的參數(shù)需要在參數(shù)自整這些場(chǎng)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。</p><p>　　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)構(gòu)和學(xué)習(xí)算法是簡(jiǎn)單明了，因此神經(jīng)網(wǎng)絡(luò)是不依賴于控制對(duì)象的模式 。</p><p>　　在本文中

8、，通過(guò)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(gè)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。</p><p>　　2 無(wú)刷直流電動(dòng)機(jī)的數(shù)學(xué)模型</p><p>　　無(wú)刷直流電機(jī)產(chǎn)生的梯形反電動(dòng)勢(shì)和施加的電流波形都是矩形波.其中自感為L(zhǎng)，互感為M。因此，三相定

9、子電壓平衡方程可以由以下狀態(tài)方程來(lái)表示：</p><p>　　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(dòng)勢(shì)；代表微分算子。</p><p>　　無(wú)刷直流電機(jī)的電磁轉(zhuǎn)矩由在定子繞組的電流和磁場(chǎng)在轉(zhuǎn)子磁鐵的相互作用下產(chǎn)生。該電磁轉(zhuǎn)矩方程：</p><p>　　式中， 代表極數(shù)； 代表總導(dǎo)體數(shù)

10、;代表電機(jī)的機(jī)械角速度。</p><p><b>　　3 控制方案</b></p><p>　　如圖1所示，一個(gè)雙閉環(huán)控制與級(jí)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(jì)來(lái)提高無(wú)刷直流電機(jī)的伺服系統(tǒng)的靜態(tài)和動(dòng)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(hào)。</p><p>　　在本文中

11、，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。</p><p><b>　　3.1有源抗擾控制</b></p><p>　　自抗擾控制器主要由三個(gè)部分組成： “轉(zhuǎn)型過(guò)程安排” 。 “非線性反饋”和“擴(kuò)展?fàn)顟B(tài)觀察”。</p><p>　　圖1無(wú)刷直流電動(dòng)機(jī)調(diào)速系統(tǒng)的原理圖</p>&

12、lt;p>　　圖2基于BP神經(jīng)網(wǎng)絡(luò)的自抗擾控制器的原理圖</p><p>　?。? ）轉(zhuǎn)型過(guò)程安排</p><p>　　式中， 為控制目標(biāo)； 為的軌道信號(hào)；是一個(gè)時(shí)間最優(yōu)集成功能，其詳細(xì)方程表達(dá)式如方程（1）所示：</p><p><b>　　（2）擴(kuò)展?fàn)顟B(tài)觀察</b></p><p>　　式中， 代表控制周期。&

13、lt;/p><p><b>　?。?）非線性反饋.</b></p><p>　　式中的參數(shù)可以在文獻(xiàn)中其他地方找到。</p><p>　　3.2 BP神經(jīng)網(wǎng)絡(luò)的參數(shù)設(shè)定</p><p>　　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(gè)參數(shù)是由非線性反饋所產(chǎn)生的。 </p><p>　

14、　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(duì)應(yīng)于控制器的三個(gè)可調(diào)參數(shù)，,,對(duì)系統(tǒng)的內(nèi)部擾動(dòng),通過(guò)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(duì)應(yīng)于最優(yōu)控制下的參數(shù)。 </p><p>　　三層BP神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)設(shè)計(jì),如圖3所示</p><p>　　圖3 BP神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)圖</p><p>　　圖中的rin(k)和

15、yout(k)分別代表速度指令和速度反饋。</p><p><b>　　輸入層的輸入公式：</b></p><p>　　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。</p><p><b>　　輸入與輸出的公式</b></p><p>　　式中，w代表隱含層，上級(jí)數(shù)的加

16、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 。</p><p>　　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(duì)稱S形函數(shù)。</p><p>　　輸入層與輸出層的關(guān)系</p><p>　　輸出層的輸出節(jié)點(diǎn)是三個(gè)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)。</p><p><b>　　性能指標(biāo)

17、函數(shù)</b></p><p>　　按照梯度下降法修正權(quán)函數(shù)的網(wǎng)絡(luò)功能。通過(guò)加權(quán)梯度方向搜索函數(shù)的負(fù)系數(shù)，并添加一個(gè)使慣性項(xiàng)全球最低的搜索快速收斂。</p><p>　　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。</p><p><b>　　輸出層的學(xué)習(xí)算法</b></p><p><b&g

18、t;　　4 仿真和實(shí)驗(yàn)結(jié)果</b></p><p>　　在本文中，無(wú)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(wú)刷直流電動(dòng)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</p><p><b>　　表1電機(jī)參數(shù)</b></p><p>　　4.1 系統(tǒng)的速度仿真</p><

19、;p>　　當(dāng)系統(tǒng)沒(méi)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)的自抗擾控制器具有最快的性能，并且系統(tǒng)沒(méi)有超調(diào)。</p><p>　　圖4 在額定運(yùn)行情況下仿真曲線統(tǒng)計(jì)圖</p><p>　　4.2 針對(duì)負(fù)載擾動(dòng)系統(tǒng)的穩(wěn)定性模擬</p><p>　　當(dāng)負(fù)載在時(shí)間0.07秒突然改

20、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。</p><p>　　采用三中方法對(duì)宏觀轉(zhuǎn)速曲線仿真圖</p><p>　　對(duì)微觀外部干擾的動(dòng)態(tài)速度曲線圖</p><p>　　圖5負(fù)荷變化的速度響應(yīng)曲線圖</p><p><b>　　4.3 實(shí)驗(yàn)結(jié)果</b>&l

21、t;/p><p>　　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(shí)間和每秒1.2億次浮點(diǎn)運(yùn)算。 TMS320VC33既支持C語(yǔ)言，有支持匯編語(yǔ)言編程。它可以容易的進(jìn)行復(fù)雜計(jì)算。 CYCLONEII FPGA是基于V.90的1.2nm SRAM過(guò)程與密

22、度超過(guò)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(gè)功能，它可以支持高性能DSP應(yīng)用。</p><p><b>　　圖6實(shí)驗(yàn)平臺(tái)</b></p><p>　　在實(shí)驗(yàn)中，一個(gè)恒定的速度3000r/min（額定運(yùn)行狀態(tài)） ，從開始到10ms的這段時(shí)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

23、快的性能時(shí)，系統(tǒng)沒(méi)有過(guò)沖。</p><p>　　霍爾傳感器獲得的無(wú)電刷直流電動(dòng)機(jī)，其控制系統(tǒng)的速度信號(hào)是由兩個(gè)環(huán)決定的：速度環(huán)和電流環(huán)。位置速度控制系統(tǒng)作為外回路，并且電流環(huán)充當(dāng)?shù)膬?nèi)環(huán)控制系統(tǒng)。控制方案在速度環(huán)實(shí)現(xiàn)。</p><p><b>　　圖7實(shí)驗(yàn)結(jié)果</b></p><p><b>　　5 總結(jié)</b></p

24、><p>　　本文提出了一種直流電機(jī)的動(dòng)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(dòng)機(jī)應(yīng)用到該系統(tǒng)，具有很強(qiáng)的魯棒性。同時(shí)，一種新的基于現(xiàn)場(chǎng)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(hào)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(duì)于靜態(tài)和動(dòng)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。

25、</p><p>　　來(lái)源：Zhi Liu ,Bai Fen Liu.Robust Control Strategy for the Speed Control of Brushless DC Motor,2013</p><p>　　Robust Control Strategy for the Speed Control of Brushless DC Motor</p>

26、<p>　　Zhi Liu ,Bai Fen Liu</p><p>　　Abstract：Brushless DC motor(BLDCM)speed servo system is multivariable．nonlinear </p><p>　　and strong coupling．The parameter variation．the cogging torque

27、 and the load disturbance easily influence its performance．Therefore it is difficult to achieve superior perform ance by using the conventional PID controller．To solve the deficiency，the paper represents the algorithm of

28、 active-disturbance rejection control(ADRC)based on back．Propagation (BP) neural network．The ADRC is independent on accurate system and its extended．</p><p>　　state observer can estimate the disturbance of t

29、he system accurately.However，the parameters of Nonlinear Feedback(NF)in ADRC are difficult to obtain．So in this paper．</p><p>　　these parameters are self-turned by the BP neural network．The simulation and ex

30、periment 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．</p><p>　　Keywords：brushless DC mo

31、tor(BLDCM)；BP(back propagation algorithms)；ADRC(active Disturbance rejection contro1)；parameters self—turning</p><p>　　1 Introduction</p><p>　　According to the properties of BLDCM ． Time-variati

32、on nonlinear and strong couple，the high performance method of speed regulation has been an essential research direction．</p><p>　　PID is a common method．However．it cannot gain the expected result to nonlinea

33、r 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 .</p><p>　　Kalman filter var

34、iable structure control ． fuzzy control,neural network control,etc．</p><p>　　The theory of auto-disturbance rejection control(ADRC)proposed these years is an easy and practical scheme．It was invented by Prof

35、． 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 influences of all internal and external disturbances

36、 inreal time when the system is activated．The control has the advantage of simple algorithm,</p><p>　　strong robustness，fast system response and high anti-interference ability ．At present．this method has bee

37、n applied to a number of fields of frontier science and technology．such as robotics，satellite attitude contro1．missile flight control， the fire control of tank and the inertia navigation．However，the parameters of ADRC ne

38、ed to be set in these occasions．The study of the parameters self-turning is only at an exploratory stage at home and abroad．</p><p>　　BP neural network has the capability of approaching to any nonlinear fun

39、ction，and its structure and learning algorithm is simple and clear ，which is not dependent on the controlled object mode1．</p><p>　　In this paper，through self-learning network，the nonlinear Feedback (NF) par

40、ameters 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 servo system in response speed， control ac

41、curacy， adaptability and robustness．</p><p>　　2 Mathematical Model of the BLDCM</p><p>　　The BLDCM produces a trapezoidal back electro motive force (EMF)．a(chǎn)nd the applied current waveform is rect

42、angular—shaped．The self-inductance is L．a(chǎn)nd the mutual inductance is M. Hence the three-phase stator voltage balance equation can be expressed by the following state equation：</p><p>　　where ,, are the phase

43、 voltage of three-phase windings. ,,are the phase current of three—phase windings；,,are the phase back EMF；is differential operator．</p><p>　　The electromagnetic torque of BLDCM is generated by the interacti

44、on of the current in stator windings and the magnetic field in rotor magnet． The electromagnetic torque equation is</p><p>　　where is pole numbers； is total conductor numbers; is mechanical angular velocity

45、of motor．</p><p>　　3 Proposed Control Scheme</p><p>　　As is shown in Fig．1，a double looped control with cascade connection has been adopted in the system． The inner loop is current loop which li

46、mits theultimate current and 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 cur

47、rent signal of the current loop．</p><p>　　In 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 i

48、s shown in Fig．2．</p><p>　　3．1 Active-Disturbance Rejection Control</p><p>　　ADRC controller consists of three main parts：“ Transition Process Arranged ”．</p><p>　　“ Nonlinear Feedb

49、ack”and“Extended—State Observer”</p><p>　　Fig．1 Schematic of BLDCM speed control system</p><p>　　Fig．2 Schematic of ADRC based on BP neural network</p><p>　　Transition Process Arran

50、ged．</p><p>　　where is the control objective； is the track signal of；is a time optimal integrated function，whose detailed expression is described as Eq．(1)．</p><p>　　Extended—State Observer(ESO

51、)</p><p>　　where is the control cycle.</p><p>　　Output of Nonlinear Feedback(NF)</p><p>　　where the parameters can be found in Ref.</p><p>　　3．2 Parameters Turned by B

52、P Neural Network</p><p>　　The parameters self-turning ADRC can be established using the BP neural network. Three parameters，,in NF are made on—line．</p><p>　　Neural network，according to the syst

53、em 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 paramet

54、ers，,Through self-learning neural networks，with the weighed coefficient of adjustment，it makes some kind of neural network output correspond to the parameters under the optimal control rate．</p><p>　　Three&#

55、183;layer BP neural network ’s structure is designed in this paper，as shown in Fig．3．</p><p>　　Fig．3 Structure of BP neural network</p><p>　　where rin(k)and yout(k)are speed command and the spee

56、d feedback．</p><p>　　The inputs of the input layer are</p><p>　　where 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．<

57、;/p><p>　　The inputs and the outputs are</p><p>　　where w are the weighted coefficients of the hidden layer．Upper numbers are the input，output，and the hidden layer． In the paper，the node of the hid

58、den layer is set to 3．</p><p>　　The activation function of the hidden layer neuron uses the symmetric sigmoid function with positive and negative feature．</p><p>　　The input and the output of th

59、e output layer are</p><p>　　The output nodes of the output layer are three adjustable parameters，,． The activation function of the output layer neuron uses the sigmoid function with positive feature．</p&g

60、t;<p>　　The performance index function is</p><p>　　In accordance with the gradient descent method to amend the network function of the weight function．The negative coefficient of the function by a wei

61、ghted gradient direction search， and add one to make the search fast convergence of the global minimum of the inertia term ．</p><p>　　whereis the learning rate and set to 0．3，and is the coefficient and set t

62、o 0．8．</p><p>　　The learning algorithm of the output layer is</p><p>　　4 Simulation and Experimental Results</p><p>　　In this paper， the simulation model of servo system for brushle

63、ss DC motor has been established in Matlab／Simulink． The actual parameters used for brushless DC motor can be taken reference for simulation ones，as shown in Table 1</p><p>　　Table 1 Motor parameters</p&g

64、t;<p>　　4．1 Rapidity of the System Due to the Simulation</p><p>　　When the system has no load． the simulation of three controlling methods is used. The given speed is 3000 r／min(the rated running stat

65、e)．The simulation results are shown in Fig．4． The results show that the ADRC based on BP neural network system has the fastest performance when the system has no overshoot.</p><p>　　Fig．4 Simulation curves i

66、n the rated running stat</p><p>　　4．2 Stability of the System Against Load Disturbance Due to the Simulation</p><p>　　When the load suddenly changes to 0．25 N ·m at time 0．07 s，the velocity

67、 curves are shown in Fig．5．The simulation results show that the ADRC based on BP neural network system has the highest stability．</p><p>　　Rotate speed curve when adopt three method on macroscopic view</p

68、><p>　　Dynamic speed curve due to external disturbance on microscopic view</p><p>　　Fig．5 Speed response curve due to variable loads</p><p>　　4．3 Experimental Results</p><p&

69、gt;　　A novel hardware structure based on DSP and FPGA is given in Fig．6． Hardware architecture of this controller is based on TMS320VC33 DSP and CYCL0NE II FPCA.TMS320VC33 is a high performance DSP with 32一bit floating—p

70、oint， 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. CYCL0N

71、EII FPGA is based on a 1．2 V．90 nm SRAM process with densities ov</p><p>　　Fig．6 Experimental platform</p><p>　　In the experiment， a constant given speed 3000 r／rain(the rated running state)is s

72、tarted at the time of 10 ms． The experimental results are shown in Fig．7．The results show that the ADRC based on BP neural network system has the fastest performance when the system has no overshoot．</p><p>

73、　　A hall sensor obtains the speed signal of the brushless DC motor．The control system is composed of two loops：speed and current loops．The position speed is the outer loop of the control system, and the current loop serv

74、e as the inner loop of the control system．The proposed control scheme is implemented on the speed loop．</p><p>　　Fig．7 Experimental results</p><p>　　5 Conclusions</p><p>　　This pape

75、r has proposed a dynamic model of BLDC and has put forward a novel control scheme according to this mode1．In the algorithm．ADRC is applied to the system，which has strong robustness．Simultaneously，a novel hardware structu

76、re of motor control system based on field programmable gate array (FPGA) and digital signal processor(DSP) are implemented to realize the proposed algorithm． The simulation and experiment results validate the scheme prop