外文翻譯---增益規(guī)劃的模糊溫度控制器的單向輸入系統(tǒng)

1、<p>　　增益規(guī)劃的模糊溫度控制器的單向輸入系統(tǒng) </p><p>　　摘要：在許多化工和半導體的生產(chǎn)過程中，溫度是獲得所需產(chǎn)品質(zhì)量的一個非常重要的控制參數(shù)。一般來說，溫度控制系統(tǒng)擁有非線性時變、慢響應、時延、單向輸入控 制的特點。一般很難估計它的精確動態(tài)模型，因此也很難設計一個通用的溫度控制器去獲得好的控制效果。增益規(guī)劃的概念是指為了獲得較好的控制性能而在控制的過程中調(diào)整隸屬函數(shù)的變化范圍。它非常適

2、合工業(yè)上的溫度控制系統(tǒng)。 </p><p>　　關(guān)鍵詞： 模糊控制；增益規(guī)劃； 單向輸入的溫度控制 </p><p>　　增益規(guī)劃模糊邏輯控制器 </p><p>　　因為PID溫度控制系統(tǒng)擁有明顯的延時、單向輸入非線性行為，因此對于基于精確模型的控制起來說，很難去估計一個適合的動態(tài)模型。此外，由于系統(tǒng)的延時、單相加熱輸入等動態(tài)特征，溫度控制系統(tǒng)的超調(diào)瞬態(tài)響應很難

3、去避免和消除。因此，怎樣設計一個通用的溫度控制器擁有很小的超調(diào)、快速的響應特性將會是一個挑戰(zhàn)性的研究課題。這里給出的不需要特定模型的增益規(guī)劃的模糊控制策略將會解決這個問題?？刂颇K圖如圖1所示。 </p><p>　　通常模糊控制方法的動機是知識的不足、動態(tài)模型的不確定性。采用模糊集理論來模擬人類的邏輯推理。模糊控制器的主要組成部分是一套語言學的模糊控制規(guī)則和用來解釋這些模規(guī)則的推理機。 </p>

4、<p>　　這種模糊控制規(guī)則提供了語言控制知識的專家和自動控制策略的催化劑之間的轉(zhuǎn)化。每一條控制規(guī)則都由一個前件和一個后件組成；一種通用的規(guī)則形式可以表示為： </p><p>　　Ri: IF X is A 1 and Y si A2 ,THEN U is C1 (1) </p><p>　　其中R 是指第i條規(guī)則，X和Y是系統(tǒng)輸入變量，U是輸出變量。

5、A1 , A2 和C1 是系統(tǒng)相應的輸入輸出論域的子集。 </p><p>　　每一條控制規(guī)則輸出的重要性依賴于輸出輸出語言變量的隸屬函數(shù)。在這種控制系統(tǒng)中，模糊控制器有兩種控制輸入，分為誤差e 和誤差變化ce ，輸出是控制電壓u 。為了簡化模糊控制器的計算，輸入變量e 和ce 的隸屬函數(shù)都選用七種平等三角形的隸屬函數(shù)。分別為NB,NM,NS,ZO,PS,PM和PB 。這種模糊變量的隸屬函數(shù)如圖4所示。這些隸屬函

6、數(shù)的分部可以放大或者縮小通過改變隸屬函數(shù)的尺度參數(shù)。增益的制度參數(shù)一般是和相應的輸入變量的標稱范圍相匹配。人類的直覺,當溫度誤差大增大時控制電壓應該增加,以提供更多的能量來加熱溫度控制腔和減少誤差。另一方面，當誤差接近隸屬函數(shù)零的子集時，控制器就應該提供微調(diào)以改變溫度誤差并減少超調(diào)的趨勢。這些映射參數(shù)被指定為ge, gce 和gu分別指誤差，誤差變化和控制輸出，這些可在表1中查閱。 </p><p>　　參數(shù)ge

7、 和gce 是分別指輸入變量溫度誤差和誤差變化的變化范圍的比例因子。參數(shù)gu 是設計來調(diào)整模糊邏輯控制電壓和簡化調(diào)試難度的量化因子。這種方法是一種新的增益規(guī)劃模糊控制結(jié)構(gòu)。這些參數(shù)的取值對于增益規(guī)劃模糊邏輯控制器來說不是非常的重要。他們可以通過簡單的實驗整定。 </p><p>　　然后，相同的價值可以應用到不同的溫度設定階躍響應和適當?shù)姆€(wěn)態(tài)精度。對于這個溫度控制系統(tǒng)，粗調(diào)的話ge =5,gce =2微調(diào)ge =

8、2,gce =1可以被應用到不同的溫度設定值。相應的模糊隸屬函數(shù)覆蓋的溫度誤差變化范圍是粗調(diào)6度，微調(diào)2.4度，如圖5所示。控制軟件的程序根據(jù)誤差反饋信號可以自動切換在粗調(diào)和微調(diào)之間?？刂圃鲆鎔u依賴于穩(wěn)定的設定值。相應的模糊隸屬函數(shù)覆蓋的溫度誤差變化范圍是粗調(diào)6度，微調(diào)2.4度，如圖2所示。控制軟件的程序根據(jù)誤差反饋信號可以自動切換在粗調(diào)和微調(diào)之間。</p><p>　　圖1 溫度控制系統(tǒng)的模糊控制模塊圖 &

9、lt;/p><p>　　圖2 模糊輸入輸出變量的隸屬函數(shù) </p><p>　　表1 模糊增益比例因子 </p><p>　　控制增益gu 依賴于穩(wěn)定的設定值。它需要反復的測試工作去尋找合適的給定的溫度的增益變化范圍，例如，50-80度，80-120度，120-150度，等等，這些參數(shù)的取值不是很重要。每一個增益參數(shù)都有確定的動態(tài)響應的變化范圍。否則，我們對于每一個設定

10、的溫度設計不同的模糊邏輯控制規(guī)則表。那是一份非常耗時間和無聊的工作。 </p><p>　　在這研究中，整個隸屬函數(shù)的論域被分成兩部分，微調(diào)部分和粗調(diào)部分。如圖5所示。在階躍響應剛開始的時候，控制器會自動的選擇粗調(diào)部分的論域去響應較大的誤差。當溫度接近給定狀態(tài)時，控制器就會切換到隸屬函數(shù)的微調(diào)論域去矯正穩(wěn)態(tài)誤差。這種控制策略可以根據(jù)反饋的控制信號e 和ce，通過切換隸屬函數(shù)的不同論語，自動的在不同的控制范圍之間切

11、換。此外，系統(tǒng)的熱量均衡控制電壓包括模糊控制規(guī)則表去代替附加的控制算法的補償，簡化控制器的設計問題和控制法則的計算。 </p><p>　　在這篇文章中，采用49條模糊控制規(guī)則，通過調(diào)整控制整流器的輸入電壓，去控制鐵室內(nèi)的溫度。這些模糊規(guī)則如表2 中所示。這些規(guī)則的確定依賴于PID控制的響應和確定的測試過程。為了平衡系統(tǒng)的的熱量，補償控制電壓直接加到額控制規(guī)則中。</p><p><

12、b>　　圖3 隸屬函數(shù)分割</b></p><p>　　模糊控制器源于控制電壓的自動調(diào)節(jié)，它包括補償控制電壓的熱量均衡，控制電壓的誤差校正。然而，PID控制器需要仔細去設計微分系數(shù)，通過對每一個給定值乏味的反復調(diào)試來獲得合適的瞬態(tài)響應。否則，溫度響應的速度在達到給定值之前將會變慢或是將會有一個非常長遠的調(diào)節(jié)階段。本文使用的隸屬函數(shù)是三角形的隸屬函數(shù)。函數(shù)可以表示為：</p><

13、;p><b>　　(2)</b></p><p>　　其中w 是隸屬函數(shù)的分布跨度，x 是模糊輸入變量，a 是值1隸屬函數(shù)相應的參數(shù)。 </p><p>　　采用高斯解模糊法得出輸出電壓信號去控制溫度控制系統(tǒng)的控制整流器的驅(qū)動。相關(guān)的 </p><p><b>　　方程是：</b></p><p&

14、gt;<b>　　(3)</b></p><p>　　其中μAij (xj ) 是模糊集合變量的語言值，ω是相應的控制規(guī)則的權(quán)值，у為純粹的模糊控制行為。從方程計算出來的模糊控制器的輸出決定著控制整流器的控制電壓的每一個控制步驟。 </p><p>　　表2 加熱器的模糊規(guī)則表 </p><p>　　Gain-scheduling fuzz

15、y temperature controller </p><p>　　for one-way input system </p><p>　　Abstract: In many chemical and semiconductor manufacturing processes, temperature is an important control parameter for obta

16、ining the desired product quality. Generally, the temperature control system has non-linear time-varying, slow response, time-delay and one-way control Input characteristics. It is difficult to estimate accurately the dy

17、namic model and design a general-purpose temperature controller to achieve good control performance. The concept of gain scheduling is employed to adjust t</p><p>　　Key words: fuzzy control; gain schedulin

18、g; temperature control and one-way input. </p><p>　　Gain-scheduling fuzzy logic controlle</p><p>　　Since this temperature control system has obvious time-delay and one-way input non-linear beha

19、viour, it is difficult to establish an appropriate dynamic model for the precise model-based controller design. In addition, the overshoot transient response of the temperature control system, with time-delay and single

20、-phase heating input dynamic features, is difficult to avoid and eliminate quickly. Hence, how to design a general-purpose temperature controller with small overshoot and quick respo</p><p>　　Usually the

21、motivation of a fuzzy approach is that the knowledge is insufficient and the dynamic model has uncertainty. Fuzzy set theory was employed to simulate the logic reasoning of human beings. The major components of a fuzzy c

22、ontroller are a set of linguistic fuzzy control rules and an inference engine to interpret these rules.</p><p>　　Figure 1 Fuzzy control block diagram of the temperature control system </p><p&g

23、t;　　These fuzzy rules offer a transformation between the linguistic control knowledge of an expert and the automatic control strategies of an activator. Every fuzzy control rule is composed of an antecedent and a consequ

24、ent; a general form of the rules can be expressed as Ri : </p><p>

25、;　　Ri:IF X is A1 and Y is A2.THEN U is C1 (1)</p><p>　　where R is the ith rule, X and Y are the states of the system output to be controlled and U is the control input. A1, A2 and C1 are

26、 the corresponding fuzzy subsets of the input and output universe of discourse, respectively. </p><p>　　The output importance of each fuzzy rule depends on the membership functions of the linguistic input an

27、d output variables. In this control system, two input indices of the fuzzy controller are temperature error e and error change ce, and the output index is the control voltage u. In order to simplify the computation of th

28、e fuzzy controller, seven equal span triangular membership functions are employed for fuzzy controller </p><p>　　input variables e and ce. They are NB, NM, NS, ZO, PS, PM and PB. The membership functions of

29、these fuzzy variables are shown in Figure 2. The divisions of this membership functions can be expanded or shrunk by changing the scaling parameters of membership functions. The gain scaling parameter is used to map the

30、 corresponding variable into this nominal range. In human beings’intuition, when </p><p>　　the temperature error is large, the control voltage should be increased to provide more energy to heat the control

31、 chamber and reduce the temperature error. On the other hand, when the error is approaching to the zero subset of membership functions, the controller should provide fine-tuning to correct he little change of temperature

32、 error</p><p>　　Figure 2 Fuzzy input and output variables membership functions </p><p>　　and reduce the overshoot tendency. These two conditions can be traded off by scaling the divided spans

33、of membership functions with a gain parameter. These mapping parameters are specified as ge, gce and gu for the error, error change and control voltage, respectively, whose values are listed in Table 1. </p><

34、;p>　　The parameters ge and gce are scaling factors selected to specify the fuzzy input variables operating ranges of temperature error and error change, respectively. The parameter gu is a gain designed to adjust the

35、 fuzzy logic control voltage and simplify the trail-and-error effort for designing the fuzzy rules table. This approach is a new gain-scheduling fuzzy control structure. These parameter values are not critical for this

36、gain-scheduling fuzzy logic controller. They can be roughly determin</p><p>　　Table 1 Fuzzy gains scaling factors</p><p>　　simple experimental tests. Then the same values can be applied to d

37、ifferent temperature setting points step response control with appropriate steady-state accuracy. For this temperature control system, ge =5 and gce =2 for the coarse-tuning operation, and ge=2 and gce=1 for the fine-tun

38、ing operation can be used in any different temperature setting points. The corresponding fuzzy membership functions covering ranges of temperature control errors are 68C for the coarse-tuning and 2.4○C for the fi</p&g

39、t;<p>　　In this study, the whole universe of discourse of the membership functions were divided into two divisions, the fine-tuning and coarse-tuning areas. Figure 5 shows the individual spans of two sets of diffe

40、rent membership functions. In the beginning of a temperature step response, the controller would automatically choose a large division of membership (coarse-tuning area) in response to the large error. When the temperatu

41、re converges and approaches the steady state, the controller would switch t</p><p>　　In this paper, 49 fuzzy rules are employed to control the chamber temperature by regulating the SCR input voltage. Those f

42、uzzy rules are listed in Table 2. These rules are established based on the testing responses of a PID control and certain trial- and-error processes. The offset control voltage for dealing with the system heat </p&

43、gt;<p>　　Figure 5 Membership functions division </p><p>　　equilibrium is added into these fuzzy rules directly. The fuzzy controller will derive </p><p>　　the control voltage automatic

44、ally, which includes the heat equilibrium offset control </p><p>　　voltage and the error correction control voltage. However,a PID controller needs carefully to design the Ki gain by tedious trial-and-error

45、for each temperature step </p><p>　　response to achieve the appropriate transient response. Otherwise, the temperature </p><p>　　response speed will have be slowed down before it reaches the spe

46、cified value or have </p><p>　　long period of oscillation. The membership function used in this paper for the fuzzification is of a triangular type. The function can be expressed as</p><p><b

47、>　　(2)</b></p><p>　　where w is the distribution span of the membership function, x is the fuzzy input variable and a is the parameter corresponding to the value 1 of the m

48、embership function. The height method is employed to defuzzify the fuzzy output variable to obtain the control voltage of the SCR heater driver of</p><p>　　Table 2 Fuzzy rules table for the he

49、ater </p><p>　　this temperature control system. </p><p>　　The relevant equation is </p><p><b>　　(3)</b></p><p>　　WhereμAij(xj) is the linguistic value of th

50、e fuzzy set variable and ωi is the weight of the corresponding rule which has been activated. Уi is the resulting fuzzy control value of the ?th fuzzy rule and у is the net fuzzy control action. The fuzzy controlle