外文翻譯--永磁同步電機(jī)高性能速度控制的最優(yōu)模糊pi控制器

1、<p><b>　　中文3150字</b></p><p>　　An Optimal Fuzzy-PI Controller for the High-Performance Speed Control of a PMSM</p><p>　　Abstract—The purpose of this paper is to present an adapti

2、ve method for improving the control performance of permanent magnetic synchronous motor (PMSM) in operating condition. The approach allows to reduce speed tracking error and to cope with external disturbance. The methodo

3、logy of speed control is presented in detail and two controllers are tested, traditional proportional integrative (PI) controller and fuzzy proportional integrative (fuzzy-PI) controller. Both controllers showed good res

4、ul</p><p>　　Keywords-Fuzzy-PI; Speed Control; Disturbance; PMSM</p><p>　　Introduction</p><p>　　High-performance servo system for permanent magnetic synchronous motor (PMSM) is essen

5、tial in many applications in the field of mechatronics such as precision engineering, computer numerically controlled machine tools and other applications in a variety of automated industrial plants . Due to the uncertai

6、nties, which are composed of unpredictable plant parameter variations, load disturbances, and nonlinear dynamics of the plant , the control performance of PMSM servo system is influenced serio</p><p>　　Up

7、to now, a large number of control techniques (fuzzy, PI, PID, etc.) with varying complexity have been proposed . Fuzzy control was first introduced and implemented in the early 1970 in an attempt to design controllers fo

8、r systems that are structurally difficult to model due to naturally existing nonlinearities and other modeling complexities. Sant et al. present the vector control of PMSM with hybrid fuzzy PI speed controller with switc

9、hing functions calculated based on the weights. Yen-Shin </p><p>　　The performance of the fuzzy-PI controllers also depends on the choice of a suitable optimization algorithm. In this paper, an adaptive spee

10、d controller is proposed to minimize or</p><p>　　eliminate the speed tracking error. The designed hybrid fuzzy-PI controller improves system performance in the transient and steady state. This paper is organ

11、ized as follows. In section 2, the vector control and disturbance effects for PMSM are described in detail. The adaptive fuzzy-PI controller is explained in section 3, whilst experimental results are presented in section

12、 4 and conclusions are drawn in the final section.</p><p>　　Pmsm vector control</p><p>　　In the PMSM, excitation flux is set-up by magnets; subsequently magnetizing current is not needed from th

13、e supply . This easily enables the application of the flux orientation mechanism by forcing the magnetizing current component of the stator current vector to be zero. As a result, the electromagnetic torque will be direc

14、tly proportional to the torque current component of the stator current vector, hence better dynamic performance is obtained by controlling the electromagnetic torque separatel</p><p><b>　　ωr </b>

15、;</p><p><b>　　ω f</b></p><p><b>　　iqr</b></p><p><b>　　idr = 0</b></p><p><b>　　iqf</b></p><p><b>　　id

16、f</b></p><p><b>　　ia</b></p><p><b>　　ib</b></p><p>　　Figure 1. The system configuration of a vector control PMSM</p><p>　　Speed control sy

17、stem of PMSM is also multi-variable, nonlinear, strong-coupled system, and the disturbances mainly include the load inertia and load torque. In the running of servo system, system inertia may change. When the system iner

18、tia increases, the response of servo system will slow down, which is likely to cause system instability and result in climb. On the contrary, when the system inertia decreases, dynamic response will speed up with speed o

19、vershoot as well as turbulence. Meanwhile, t</p><p>　　Design of speed controller</p><p>　　In this paper, we are proposing a speed control scheme based on fuzzy logic to improve the control perfo

20、rmance for PMSM. Speed controller can be implemented using several approaches, such as PI, fuzzy, etc. However, when implementing a speed controller the following conditions should be considered:</p><p>　　Si

21、mplicity: The speed control law must be simple and easy to compute in order to enable fast servo adaptation.</p><p>　　PI-type control: In order to achieve a null steady state error, a PI type speed control l

22、aw</p><p>　　should be selected and implemented.</p><p>　　Implementation requirements should not include significant changes to the original control system.</p><p>　　Given our object

23、ive and system requirements, two control algorithms, PI and fuzzy logic, are chosen. The choice for PI controller is due to its good performance when applied in practical situations, and the preference for fuzzy controll

24、er is due to no requirement of the rigorous mathematical system model.</p><p>　　Fuzzy Control Architecture</p><p>　　Fuzzy logic was conceived to apply a more human-like way of thinking in comput

25、er programming. It is ideal for controlling nonlinear systems and model complex systems where ambiguity is common. It is also potentially very robust, maintaining good closed-loop system performance over a wide range of

26、operating conditions. In our system, speed controller input variables are the speed error e and change of the speed error de :</p><p>　　e(k) = ωr (k) ? ω f (k )</p><p><b>　　(1)</b&g

27、t;</p><p>　　de(k) = e(k ) ? e(k ?1)</p><p><b>　　(2)</b></p><p><b>　　Where ωr</b></p><p>　　is the speed command and ω f</p><p>　　i

28、s the actual speed.</p><p><b>　　Fuzzy-PI</b></p><p>　　From the conventional PI control algorithm, we can obtain the following discrete equations:</p><p>　　iq

29、(k) = iq?1 (k) + Δiq (k)</p><p><b>　　(3)</b></p><p>　　Δiq (k) = k p de(k) + ki e(k)</p><p><b>　　(4)</b></p><p>　　If e and de are fuzzy variables

30、, (3) and (4) become a fuzzy control algorithm. Then, the centre of area method is selected for defuzzify the output fuzzy set inferred by the controller:</p><p><b>　　Δiq</b></p><p>

31、<b>　　n η Δ</b></p><p>　　=i=1 i q</p><p><b>　　n</b></p><p><b>　　i=1 i</b></p><p><b>　　(5)</b></p><p

32、><b>　　Where ηi</b></p><p>　　is the membership function, which takes values in the interval [0, 1].</p><p>　　Knowledge Base</p><p>　　The knowledge base of fuzzy logic

33、 controller is composed of two components, namely, a database and a fuzzy control rule base. The well-known PI-like fuzzy rule base is used in this paper (Table 1). The surface of rule base is shown in Fig. 2. It allows

34、fast working convergence without significant oscillations and prevents overshoots and undershoots.</p><p>　　TABLE 1 FUZZY RULE BASE</p><p><b>　　e</b></p><p>　　deNLNM

35、NSZRPSPMPL</p><p>　　NLnlnlnlnlnmnsze NMnlnlnmnmnszeps NSnmnmnmnszepsps NZnmnmnszepspspm PZnmnsnszepspmpm PSnsnszepspmpmpm PMnszepspmpmplpl PLzepspm

36、plplplpl</p><p>　　Figure 2. The surface of rule base</p><p>　　Tuning Strategy</p><p>　　Fuzzy logic design is involved with two important stages: knowledge base design and tuning.

37、 However, at present there is no systematic procedure to do that. The control rules are normally extracted from practical experience, which may make the result focused in a specific application. The objective of tuning i

38、s to select the proper combination of all control parameters so that the resulting closed-loop response best meets the desired design criteria.</p><p>　　In order to adapt servo system to different disturbanc

39、es, the scaling factors should be tuned. The controller should also be adjusted with characteristics representing the scenario to be controlled. These adjustments can be made through the scaling factors, usually applied

40、in any PI controller. S.T. Lin et al. [10] proposed an adjustment where the scaling factors are dynamic and thus they have been adjusted along the task. In this paper, the scaling factors are set to appropriate constant

41、values</p><p>　　Experiment</p><p>　　The apparatus for the experiment contains three major parts and some data transferring buses, as shown in Fig. 3. These three major parts are: 1) a PC and a P

42、CI with sampling time equal to 1ms; 2) AC servo drive using a DSP plus a FPGA, where DSP TMS320F2812 mainly accomplishes position, velocity and torque control, and FPGA EP2C8Q208C8N is responsible for the analysis and re

43、alization of absolute ruler and NCUC-Bus protocols; 3) PMSM with the parameters described in Table 2. Through the PCI cont</p><p>　　and control parameters to servo drive, and receives expected torque current

44、 and feedback velocity from servo drive for the model identification.</p><p>　　Figure 3. The apparatus for the experiment TABLE 2 MOTOR PARAMETERS</p><p>　　Motor Rating</p><p>　　T

45、orquecoefficient</p><p><b>　　0.75Nm/A</b></p><p>　　Ratedspeed</p><p><b>　　1000r/min</b></p><p>　　RatedTorque</p><p><b>　　4

46、.5Nm</b></p><p>　　Frictioncoefficient</p><p>　　0.0008Nms/rad Inertia 0.0028Nms2/rad</p><p><b>　　Poles 3</b></p><p>　　In the experimental tests withou

47、t applied load torque, a trapezium-type speed command, the maximum speed of which is 1000r/min, is applied. To evaluate the control performance, a fixed PI controller is considered. Fig. 4 shows the speed response with P

48、I controller, it indicates that the maximum speed error is about 34r/min at the acceleration stage and the maximum speed error fluctuation is about 7r/min at the constant speed stage. Speed response with fuzzy-PI contro

49、ller is shown in Fig. 5, it </p><p>　　In the experimental tests without applied load torque, a trapezium-type speed command, the maximum speed of which is 1000r/min, is applied. To evaluate the control perfo

50、rmance, a fixed PI controller is considered. Fig. 4 shows the speed response with PI controller, it indicates that the maximum speed error is about 34r/min at the acceleration stage and the maximum speed error fluctuati

51、on is about 7r/min at the constant speed stage. Speed response with fuzzy-PI controller is shown in Fig. 5, it </p><p>　　maximum speed error is about 15r/min at the acceleration stage and the maximum speed e

52、rror fluctuations is about 3r/min at the constant speed stage.</p><p><b>　　1200</b></p><p><b>　　1000</b></p><p><b>　　800</b></p><p>

53、<b>　　600</b></p><p><b>　　400</b></p><p><b>　　200</b></p><p><b>　　0</b></p><p>　　0123456</p><p><b&

54、gt;　　Time (s)</b></p><p><b>　　20</b></p><p><b>　　10</b></p><p><b>　　0</b></p><p><b>　　-10</b></p><p>

55、<b>　　-20</b></p><p><b>　　-30</b></p><p><b>　　-40</b></p><p>　　0123456</p><p><b>　　Time (s)</b></p><p>

56、;　　Figure 4. The speed response with PI controller (no load torque)</p><p><b>　　1200</b></p><p><b>　　1000</b></p><p><b>　　800</b></p><

57、p><b>　　600</b></p><p><b>　　400</b></p><p><b>　　200</b></p><p><b>　　0</b></p><p>　　0123456</p><p>&

58、lt;b>　　Time (s)</b></p><p><b>　　10</b></p><p><b>　　5</b></p><p><b>　　0</b></p><p><b>　　-5</b></p><p&

59、gt;<b>　　-10</b></p><p><b>　　-15</b></p><p>　　0123456</p><p><b>　　Time (s)</b></p><p>　　Figure 5. The speed response with fuzzy

60、-PI controller (no load torque)</p><p>　　In the experimental tests with changed applied load torque, a slope-type speed command,</p><p>　　the maximum speed of which is 1000r/min, is applied. Whe

61、n</p><p>　　t = 2s , the applied load torque is</p><p><b>　　2Nm. When</b></p><p>　　t = 5s , the applied load torque is suddenly became to 8Nm. To evaluate the</p>

62、<p>　　control performance, a fixed PI controller is also considered. Fig. 6 shows the speed response</p><p>　　with PI controller. When</p><p>　　2s ≤ t < 5s , the maximum speed err

63、or is about 95r/min at the</p><p>　　acceleration stage and marked speed overshoot at the constant speed stage. When</p><p>　　5s ≤ t < 10s ,</p><p>　　it is clear that the max

64、imum speed error fluctuation is about 50r/min and the tracking response does not meet the design specifications.</p><p>　　Speed response with fuzzy-PI controller is shown in Fig. 7. When</p><p>

65、　　2s ≤ t < 5s , the maximum</p><p>　　speed error is only about 48r/min at the acceleration stage and unobvious speed overshoot at</p><p>　　the constant speed stage. When</p>&l

66、t;p>　　5s ≤ t < 10s</p><p>　　, it is clear that the maximum speed</p><p>　　error fluctuations is only about 8r/min. servo system with fuzzy-PI controller has better speed trac

67、king performance and can suppress the load torque well.</p><p><b>　　1200</b></p><p><b>　　1000</b></p><p><b>　　800</b></p><p><b&g

68、t;　　600</b></p><p><b>　　400</b></p><p><b>　　200</b></p><p><b>　　0</b></p><p>　　012345678910</p><p><

69、b>　　Time (s)</b></p><p><b>　　50</b></p><p><b>　　0</b></p><p><b>　　-50</b></p><p><b>　　-100</b></p><p

70、>　　012345678910</p><p><b>　　Time (s)</b></p><p>　　Figure 6. The speed response with PI controller</p><p><b>　　1000</b></p><p><b

71、>　　500</b></p><p><b>　　0</b></p><p>　　012345678910</p><p><b>　　Time (s)</b></p><p><b>　　50</b></p><p&g

72、t;<b>　　0</b></p><p><b>　　-50</b></p><p>　　012345678910</p><p><b>　　Time (s)</b></p><p>　　Figure 7. The speed response with

73、 fuzzy-PI controller</p><p>　　Conclusions</p><p>　　This paper has presented an adaptive fuzzy-PI speed control scheme for PMSM drive. The effectiveness of the proposed approach was proved throug

74、h experiments, showing that the hybrid control improves significantly servo performance, making servo system more human-like, flexible and with capacity to make decisions. Substantially, the fuzzy-PI controller can occur

75、 a small overshoot against a large overshoot when using the PI controller. Furthermore, in some situations the fuzzy-PI controller showed</p><p>　　永磁同步電機(jī)高性能速度控制的最優(yōu)模糊PI控制器</p><p>　　摘要：本文的目的是介紹改進(jìn)的

76、永磁同步電機(jī)（PMSM）工作的控制性能的自適應(yīng)方法。該方法允許降低速度追蹤誤差及應(yīng)對(duì)外部干擾。本文提出了詳細(xì)速度控制的方法，并針對(duì)兩個(gè)控制器進(jìn)行測(cè)試，傳統(tǒng)的綜合比例（PI）控制器和模糊綜合成正比（模糊-PI）控制器。從有著類似動(dòng)作的實(shí)驗(yàn)中，兩個(gè)控制器都表現(xiàn)出了良好的效果。然而，模糊-PI在某些方面脫穎而出。本文的主要目的是模糊邏輯算法的擴(kuò)展，以提高工業(yè)應(yīng)用中的伺服控制性能。</p><p>　　關(guān)鍵詞：模糊PI；

77、 速度控制； 干擾； 永磁同步電機(jī)</p><p><b>　　1 引言 </b></p><p>　　用于永磁同步電機(jī)（PMSM）的高性能伺服系統(tǒng)在機(jī)電一體化，如精密工程，電腦數(shù)控機(jī)床及各種自動(dòng)化工業(yè)廠房的其他應(yīng)用領(lǐng)域的許多應(yīng)用中是必不可少的。由于不確定性因素，例如不可預(yù)測(cè)的工廠參數(shù)變化，負(fù)載擾動(dòng)，以及固有的非線性動(dòng)態(tài)過(guò)程，永磁同步電機(jī)伺服系統(tǒng)的控制性能受到嚴(yán)重影響

78、。在這種情況下，伺服驅(qū)動(dòng)器可能需要比較快地響應(yīng)命令的變化，并對(duì)于不確定性有足夠的魯棒性。為了滿足高速、高精度的直線電機(jī)的發(fā)展要求，我們希望有一個(gè)可以針對(duì)操作環(huán)境的干擾和不確定性有更高的抗擾動(dòng)性能的智能??控制器。</p><p>　　目前為止，已經(jīng)有大量的具有不同的復(fù)雜性的控制技術(shù)（模糊，PI，PID等）被提出。模糊控制在1970年初首次被提出并實(shí)施是在一次設(shè)計(jì)控制器的實(shí)驗(yàn)中，那是在結(jié)構(gòu)上很難建模由于自然存在的非

79、線性和其他建模復(fù)雜系統(tǒng)。桑特等在永磁同步電機(jī)的矢量控制與交換計(jì)算功能的基礎(chǔ)上，提出了權(quán)重混合模糊PI速度控制器。顏善等人提出的直接轉(zhuǎn)矩控制異步電機(jī)驅(qū)動(dòng)具有快速跟蹤能力，更低的穩(wěn)態(tài)誤差，和強(qiáng)大的負(fù)載擾動(dòng)，是一種新的混PI型模糊控制器?？傊?，當(dāng)流程過(guò)于復(fù)雜時(shí)，通過(guò)常規(guī)的定量技術(shù)來(lái)分析模糊邏輯控制顯得非常有用。眾所周知，調(diào)速技術(shù)已能夠在伺服系統(tǒng)領(lǐng)域執(zhí)行越來(lái)越復(fù)雜的任務(wù)。</p><p>　　模糊-PI控制器的性能還取決

80、于選擇合適的優(yōu)化算法。在本文中，提出了一種自適應(yīng)速度控制器，它以最小化或消除的速度跟蹤誤差，所設(shè)計(jì)的混合模糊PI控制器改善了瞬態(tài)和穩(wěn)態(tài)系統(tǒng)性能。本文的結(jié)構(gòu)如下：第2節(jié)詳細(xì)描述了永磁同步電機(jī)矢量控制和干擾的影響，第3節(jié)解釋了自適應(yīng)模糊PI控制器的，第4節(jié)列出了實(shí)驗(yàn)結(jié)果，最后一節(jié)得出了結(jié)論。</p><p>　　2 永磁同步電機(jī)矢量控制 </p><p>　　對(duì)于永磁同步電機(jī)，磁鐵建立勵(lì)磁磁通

81、，隨后的磁化電流不從供給獲得。這很容易通過(guò)迫使定子電流向量的勵(lì)磁電流分量為零使磁場(chǎng)定向的機(jī)制作用。其結(jié)果是，電磁轉(zhuǎn)矩通過(guò)分別控制電磁轉(zhuǎn)矩，將定子電流向量和扭矩電流分量作用成正比，因此獲得更好的動(dòng)態(tài)性能。矢量控制的永磁同步電動(dòng)機(jī)伺服系統(tǒng)的系統(tǒng)結(jié)構(gòu)如圖1所示，在矢量控制方案中，扭矩控制可以進(jìn)行對(duì)定子電流向量的適當(dāng)調(diào)節(jié)，這意味著精確的速度控制取決于電流矢量的調(diào)節(jié)。</p><p>　　圖1.矢量控制的永磁同步電機(jī)的系統(tǒng)

82、配置</p><p>　　永磁同步電機(jī)調(diào)速系統(tǒng)是多變量、非線性、強(qiáng)耦合的系統(tǒng)，其干擾主要包括負(fù)載慣量和負(fù)載轉(zhuǎn)矩。在伺服系統(tǒng)的運(yùn)行，系統(tǒng)的慣性可能會(huì)改變。當(dāng)系統(tǒng)慣量增大，伺服系統(tǒng)的響應(yīng)會(huì)變慢，這很可能會(huì)導(dǎo)致系統(tǒng)不穩(wěn)定。反之，當(dāng)系統(tǒng)的慣性減小，動(dòng)態(tài)響應(yīng)將加快、速度超調(diào)。同時(shí)，伺服系統(tǒng)的主要作用是驅(qū)動(dòng)負(fù)載的運(yùn)行，但在許多行業(yè)中，通過(guò)伺服系統(tǒng)承載的負(fù)荷不是恒定的，變動(dòng)負(fù)載轉(zhuǎn)矩將不會(huì)對(duì)伺服控制性能顯著的影響：在伺服系統(tǒng)中，

83、突然增加或減少負(fù)載轉(zhuǎn)矩的運(yùn)行會(huì)導(dǎo)致伺服調(diào)速的波動(dòng)，影響了定位和控制性能的準(zhǔn)確性。</p><p>　　3 速度控制器的設(shè)計(jì) </p><p>　　在本文中，我們提出一種基于模糊邏輯、以改善永磁同步電機(jī)控制性能速度控制方案。速度控制器可以使用幾種方法，如PI，模糊等。然而，實(shí)施速度控制時(shí)，應(yīng)考慮下列情況： </p><p>　　? 簡(jiǎn)單：速度控制算法必須是簡(jiǎn)單且容易

84、計(jì)算，以實(shí)現(xiàn)快速的伺服適應(yīng)。 </p><p>　　? PI型控制：為了實(shí)現(xiàn)零穩(wěn)態(tài)誤差，應(yīng)該選擇和實(shí)施PI型轉(zhuǎn)速控制規(guī)律。 </p><p>　　? 實(shí)施要求不應(yīng)該包括明顯改變?cè)械目刂葡到y(tǒng)。 </p><p>　　由于目標(biāo)和系統(tǒng)需求，我們選擇兩種控制算法——PI和模糊邏輯。當(dāng)在實(shí)際情況中對(duì)于PI控制器的選擇是由于其良好的性能，而傾向于選擇模糊控制器是由于其對(duì)于

85、嚴(yán)格的數(shù)學(xué)系統(tǒng)模型的不作要求。</p><p><b>　　A.模糊控制架構(gòu) </b></p><p>　　模糊邏輯的構(gòu)想采用更類似人類在計(jì)算機(jī)編程的思維方式。它在理想的非線性控制系統(tǒng)和模型的復(fù)雜系統(tǒng)中歧義很常見。它也可能保持在一個(gè)寬范圍的操作條件良好的閉環(huán)系統(tǒng)的性能。在我們的系統(tǒng)中，速度控制器的輸入變量是速度誤差和速度誤差的變化：</p><p&

86、gt;<b>　　(1)</b></p><p><b>　　(2)</b></p><p>　　其中，是速度指令，是實(shí)際速度。</p><p><b>　　B.模糊PI </b></p><p>　　由傳統(tǒng)的PI控制算法，我們可以得到如下離散方程：</p>&l

87、t;p><b>　　(3)</b></p><p><b>　　(4)</b></p><p>　　如果e和de是模糊變量，由（3）和（4）得到一個(gè)模糊控制算法。然后，選擇區(qū)域方式的中心為defuzzify的輸出模糊集合，控制器可得：</p><p><b>　　(5)</b></p>

88、;<p>　　其中是隸屬函數(shù)，在 [0,1]中取值。 </p><p><b>　　C.知識(shí)庫(kù) </b></p><p>　　模糊邏輯控制器的知識(shí)庫(kù)由兩部分組成，即一個(gè)數(shù)據(jù)庫(kù)和模糊控制規(guī)則庫(kù)。眾所周知，PI模糊規(guī)則庫(kù)是如本文中（表1），規(guī)則庫(kù)中的表面如圖2所示，它使快速的工作交替不產(chǎn)生顯著波動(dòng)，防止超調(diào)和下沖。 </p><p>

89、<b>　　表1.模糊規(guī)則庫(kù)</b></p><p>　　圖2 .規(guī)則庫(kù)的表面</p><p><b>　　D.調(diào)整策略</b></p><p>　　模糊邏輯的設(shè)計(jì)涉及兩個(gè)重要階段：知識(shí)庫(kù)的設(shè)計(jì)和調(diào)整。然而，目前還沒(méi)有系統(tǒng)的程序來(lái)做到這一點(diǎn)?？刂埔?guī)則通常由實(shí)際經(jīng)驗(yàn)提取的，這可能使結(jié)果集中在一個(gè)特定的應(yīng)用程序。調(diào)諧的目的是選

90、擇所有控制參數(shù)的適當(dāng)組合，使所得到的閉環(huán)響應(yīng)最佳地滿足所需的設(shè)計(jì)準(zhǔn)則。</p><p>　　為了使伺服系統(tǒng)適應(yīng)不同的干擾，比例因子應(yīng)該進(jìn)行調(diào)整。該控制器還應(yīng)根據(jù)不用情況下被控制量的特點(diǎn)進(jìn)行調(diào)整。這些調(diào)整可以通過(guò)PI控制器的應(yīng)用進(jìn)行縮放因子。S.T. Lin等人提出了一種調(diào)整，其中的縮放因子是動(dòng)態(tài)的，因此它們是隨著任務(wù)而調(diào)整。在本文中，縮放因子被設(shè)置為適當(dāng)?shù)暮愣ㄖ?，通過(guò)反復(fù)試驗(yàn)來(lái)實(shí)現(xiàn)。</p><

91、;p><b>　　4 實(shí)驗(yàn) </b></p><p>　　用于實(shí)驗(yàn)的裝置包含三個(gè)主要部件和一些數(shù)據(jù)傳輸總線，如圖3所示，這三個(gè)主要部分組成是：1）一臺(tái)PC與采樣時(shí)間等于1ms的一個(gè)PCI; 2）交流伺服系統(tǒng)的驅(qū)動(dòng)器由DSP和FPGA組成，其中DSP TMS320F2812的主要實(shí)現(xiàn)位置是速度和轉(zhuǎn)矩控制，F(xiàn)PGA EP2C8Q208C8N負(fù)責(zé)分析和實(shí)現(xiàn)絕對(duì)控制以及NCUC總線協(xié)議; 3）

92、永磁同步電動(dòng)機(jī)的參數(shù)如表2所示，通過(guò)PCI控制器，PC機(jī)將速度指令和控制參數(shù)發(fā)送到伺服驅(qū)動(dòng)器，并從伺服驅(qū)動(dòng)器接收期望的轉(zhuǎn)矩電流和反饋速度，用于模型識(shí)別。</p><p><b>　　圖3.實(shí)驗(yàn)的裝置</b></p><p><b>　　表2.電機(jī)參數(shù)</b></p><p>　　在不施加負(fù)載轉(zhuǎn)矩的實(shí)驗(yàn)測(cè)試中，應(yīng)用最大速度為

93、1000r/min的梯形型速度指令。為了評(píng)估其控制性能，考察固定PI控制器。圖4是具有PI控制器的速度響應(yīng)，它表示加速階段最大速度誤差約為34r/min，恒速階段最大速度誤差波動(dòng)為大約7r/min。模糊PI控制器的速度響應(yīng)如圖5所示，它具有更好的速度跟蹤性能，加速階段最大速度誤差約為15r/min，恒速階段最大速度誤差的波動(dòng)是大約3r/min。</p><p>　　圖4.使用PI控制器的響應(yīng)速度（空載轉(zhuǎn)矩）<

94、;/p><p>　　圖5.使用模糊PI控制器的響應(yīng)速度（空載轉(zhuǎn)矩）</p><p>　　在實(shí)驗(yàn)測(cè)試，改變負(fù)載轉(zhuǎn)矩，應(yīng)用最大速度為1000r/min的斜坡式速度指令。當(dāng)t=2s時(shí)，所施加的負(fù)載轉(zhuǎn)矩為2Nm。當(dāng)t=5s時(shí)，所施加的負(fù)載轉(zhuǎn)矩突變到8Nm的。為了評(píng)估其控制性能，也要考察固定PI控制器。圖6給出了具有PI控制器的響應(yīng)速度。當(dāng)時(shí)，加速階段的最大速度誤差約為95r/min，恒速階段速度超調(diào)。

95、當(dāng)時(shí)，很顯然，最大速度誤差波動(dòng)約為50r/min和跟蹤響應(yīng)不符合設(shè)計(jì)規(guī)格。  使用模糊PI控制器的速度響應(yīng)如圖7所示，當(dāng)時(shí)，加速階段的最大速度誤差僅約48r/min和恒速階段不明顯的速度超調(diào)。當(dāng)時(shí)，很顯然，最大速度誤差波動(dòng)只有約8r/min。伺服系統(tǒng)模糊PI控制器具有更好的速度跟蹤性能，并能有效的抑制負(fù)載轉(zhuǎn)矩。</p><p>　　圖6.與PI控制器的響應(yīng)速度</p><p&

96、gt;　　圖7.使用模糊PI控制器的響應(yīng)速度</p><p><b>　　5 結(jié)論 </b></p><p>　　本文介紹了永磁同步電機(jī)驅(qū)動(dòng)器的自適應(yīng)模糊PI速度控制方案。通過(guò)實(shí)驗(yàn)證明了該方法的有效性，表明混合控制顯著提高了伺服性能，使得伺服系統(tǒng)能更人性化，靈活有效的做出決定。實(shí)際上，模糊PI控制器的超調(diào)量比PI控制器的超調(diào)量小。此外，在某些情況下，為了快速達(dá)到設(shè)定點(diǎn)