電氣與信息學(xué)院自動化專業(yè)外文翻譯(畢業(yè)設(shè)計用)鍋爐蒸汽溫度模糊神經(jīng)網(wǎng)絡(luò)的廣義預(yù)測控制

1、畢業(yè)設(shè)計（論文）外文翻譯 neuro-fuzzy generalized predictive control of boiler steam temperature鍋爐蒸汽溫度模糊神經(jīng)網(wǎng)絡(luò)的廣義預(yù)測控制 本 科 電氣與信息學(xué)院 自 動 化 講 師 ： 2008年4月20日 學(xué)生姓名學(xué)歷層次所在院系所學(xué)專業(yè)指導(dǎo)教師教師職稱完成時間（本文檔前部分為中文，后部分為英文部分，后部分英文部分為pdf轉(zhuǎn)化為word版本，不清晰之處，可參考本人上傳的英文pdf版本原文，可以免費下載英文pdf版本(下載地址：鍋爐蒸汽溫度模糊神經(jīng)網(wǎng)絡(luò)的廣義預(yù)測控制 摘要：發(fā)電廠是非線性和不確定的復(fù)雜系統(tǒng)?，F(xiàn)代電廠在運作上的，

2、為確保高效率和高負(fù)荷的能力，可靠的控制過熱蒸汽溫度是必要的。本文提出了一類在非線性廣義預(yù)測控制器的基礎(chǔ)上的模糊神經(jīng)網(wǎng)絡(luò)（ nfgpc ）。所提出的非線性控制器適用于控制一臺200 mw電廠的過熱蒸汽溫度。從實驗的移植和仿真移植上獲得比傳統(tǒng)的控制器好得多的性能。 關(guān)鍵詞：模糊神經(jīng)網(wǎng)絡(luò);廣義預(yù)測控制;過熱蒸汽溫度。1 引言 這種持續(xù)不斷的電廠和電力站復(fù)雜系統(tǒng)的特點是非線性、不確定性和負(fù)載擾動。蒸汽發(fā)電的過程中鍋爐-汽輪機(jī)溫度過熱是一個重要的過程，蒸汽加熱后，進(jìn)入渦輪驅(qū)動發(fā)電機(jī)?？刂七^熱蒸汽溫度不僅是在技術(shù)上具有挑戰(zhàn)性，但在經(jīng)濟(jì)上也是十分重要的。 圖 1鍋爐過熱器和蒸汽生成過程。 從圖1看出 ，產(chǎn)生

3、的蒸汽從鍋爐汽包通過低溫過熱器后進(jìn)入輻射型屏。水變成噴涂的蒸汽，以控制過熱蒸汽的溫度。適當(dāng)?shù)目刂齐姀S過熱蒸汽溫度是極其重要的，可以確保整體效率和安全性。蒸汽溫度太高是不可取的地方，因為過熱它可損害和高壓力汽輪機(jī)，太低也不行，因為它會降低電廠效率的。減少溫度波動內(nèi)過熱也是重要的，因為它有助于減少在單位內(nèi)機(jī)械應(yīng)力造成的微裂紋，延長單位秩序壽命，并減少維修成本。作為gpc的推導(dǎo)應(yīng)該盡量減少這些波動，它是眾多的控制器是最適合實現(xiàn)這一目標(biāo)的。 多變量多步自適應(yīng)調(diào)節(jié)已適用于控制過熱蒸汽溫度在150噸/ h鍋爐 ，提出了廣義預(yù)測控制以控制蒸汽溫度。非線性長程預(yù)測控制器基于神經(jīng)網(wǎng)絡(luò)發(fā)展是以控制主蒸汽溫度和壓力

4、。控制主蒸汽壓力和溫度的基礎(chǔ)上，非線性模型的構(gòu)成是非線性靜力常數(shù)和非線性動力學(xué)。 模糊邏輯是把人類的經(jīng)驗透過模糊規(guī)則表現(xiàn)出來。然而，設(shè)計模糊邏輯控制器是有點時間消費，由于模糊規(guī)則，往往得到的試驗是錯誤的。在此相反，神經(jīng)網(wǎng)絡(luò)不僅有能力近似的非線性職能與任意精度，他們也可以有受過訓(xùn)練的實驗數(shù)據(jù)。該模糊神經(jīng)網(wǎng)絡(luò)（ nfns ）最近開發(fā)的優(yōu)勢，模型的透明度，模糊邏輯，和學(xué)習(xí)能力的神經(jīng)網(wǎng)絡(luò)。該nfns已被用來發(fā)展自我控制，因此，一個有用的工具，可以發(fā)展國家非線性預(yù)測控制。從nfns可以考慮到作為一個網(wǎng)絡(luò)構(gòu)成的幾個地方， 其中每一項包含一個局部線性模型，在非線性預(yù)測控制的基礎(chǔ)上nfns可以制訂與網(wǎng)絡(luò)，使用

5、各自的地方線性模型把當(dāng)?shù)厮械膹V義預(yù)測控制器（gpc ）的設(shè)計，。按照這一辦法，在非線性廣義預(yù)測控制器的基礎(chǔ)上， nfns ，或簡單地說， 該模糊神經(jīng)網(wǎng)絡(luò)的廣義預(yù)測控制器（ nfgpcs） 推導(dǎo)出在這里。建立控制器，然后應(yīng)用控制過熱蒸汽溫度的200 mw機(jī)組。實驗所得的數(shù)據(jù)，用來訓(xùn)練nfn模型植物，并從哪個地方gpcs組成部分的nfgpc ，然后設(shè)計。 擬議的控制器的測試首先就模擬這個過程中，在申請前，它控制火電廠。2模糊神經(jīng)網(wǎng)絡(luò)模型 考慮以下的一般單輸入單輸出非線性動態(tài)系統(tǒng)： （1） 其中f ？ 是一個平穩(wěn)的非線性函數(shù)，例如，一個泰勒一系列擴(kuò)張的存在，e( t）是一個零的意思和是差分算子，n

6、"y, n"u, n"e和d分別是延遲了該系統(tǒng)已知的命令和時間。 讓當(dāng)?shù)氐木€性模型的非線性系統(tǒng)（1）在作業(yè)點為o( t）是由以下控制自回歸綜合移動平均線（ carima ）模型： a其中，a(z1)= a(z1)，b(z1)和c(z1)是多項式在z - 1 落后的轉(zhuǎn)向。請注意，該這些系數(shù)多項式函數(shù)的轉(zhuǎn)向點為o（t）。非線性系統(tǒng)（1）分割成為幾個作業(yè)區(qū)域，如每個地區(qū)可以近似當(dāng)?shù)氐木€性模型。自nfns是一類在本地的聯(lián)想記憶網(wǎng)絡(luò)的知識存儲，他們可以應(yīng)用到模型這一類非線性系統(tǒng)。示意圖該nfn結(jié)果表明，在圖2 。b-樣條函數(shù)作為隸屬函數(shù)在nfns由于以下原因。第一， b-條

7、功能可隨時在指定的秩序的基礎(chǔ)功能和數(shù)目內(nèi)，第二，他們是界定在一個范圍內(nèi)的支持和輸出的基礎(chǔ)上功能始終是積極的，即i.e., jk(x) = 0, x / jk, j 和 jk(x) > 0, x jk, j ， 第三，根據(jù)職能的基礎(chǔ)上形成一個分割的團(tuán)體，i ,e k(x) 1, x xmin, xmax.第四，輸出的基礎(chǔ)上的功能可以得到由一個復(fù)發(fā)的方程。 圖2模糊神經(jīng)網(wǎng)絡(luò)。 隸屬函數(shù)的模糊變量的衍生從模糊規(guī)則可以得到由該變量的基礎(chǔ)上的職能。作為一個例子，考慮該nfn顯示在圖2 ，構(gòu)成以下模糊規(guī)則： 如果操作條件（ x1是積極的小，和xn 是消極的大型） ， 那么輸出是由當(dāng)?shù)豤arima模型

8、i:這里n是輸入x和p的傳染媒介維度，在nfn中重量工具的數(shù)字，它是給出的， 這里ki和ri是依據(jù)作用的命令和各自內(nèi)在結(jié)的數(shù)字。單變量的b-多槽軸的依據(jù)作用以前描述了也適用于多維分布的依據(jù)作用，nfn的產(chǎn)品是是被定義的亢奮長方形。 3神經(jīng)模糊的網(wǎng)絡(luò)廣義預(yù)測控制gpc通過使以下方式從而減到最小而獲得作用 10， 這里qj和j分別為的衡量要素的預(yù)言錯誤和控制計算， yr (t + j)是jth前面參考道路，d是極小的費用、n和m分別為最大費用預(yù)言錯誤和控制的。 控制從nfgpc的計算是被衡量的總和的從gpc的地方p控制器獲得的控制。 這里ui (t)是控制ith區(qū)域，i (x)是在(4)最早的方式

9、。 注意在的重量nfgpc與那是相同的在nfn中塑造的過程。從交換gpc控制器之間 nfgpc介入模糊的邏輯，它可以被解釋為沒有，僅僅作為一個模糊控制器，還可以作為一位模糊的監(jiān)督器。這種控制可以是光滑的，如果重量i (x)是適宜的建立，從nfn (6)和控制(8)，指定的j 通過(7)可以被重寫如下： 由于使用不平等，價值函數(shù)首先被簡化。從下面 化簡 從(10)式暗示被衡量的被擺正的的總和可以是價值函數(shù)j.重寫的一個最高界面通過(9)給出 這里 通過(11)式顯示本質(zhì)上是作為那使j.減到最小那使減到最小的ji本。 從(12)，一套地方廣義預(yù)測控制器得到 nfgpc構(gòu)成部分。 這里 和 通過計算

10、前面優(yōu)化m步控制，和使用后退前面第一步控制被實施，通過原則10，給 其中 可以簡化為 4過熱蒸汽溫度的神經(jīng)模糊塑造和有預(yù)測性的控制 讓是過熱蒸汽溫度和， 過熱蒸汽水對高溫過熱裝置的。 可以通過由二次式樣接近 11 ： (17) 式是線性模型，然而，僅一個地方模型為選擇的工作點。 因為裝載是獨特的事，它被用于選擇在nfn的地方之間分裂。 基于這種方法，如圖3所顯示，使用操作員的經(jīng)驗， 被劃分成五個地區(qū)，看待裝載200mw作為一個高度， 180mw作為中等上流，作為的160mw作為中等低落的方式、140mw和120mw如低。 為間隔時間30s，估計的線性本機(jī)塑造使用了使用了顯a (z1)在nfn在

11、表1.使用了顯示。圖三本地模式的隸屬函數(shù) 通過nfgpc的時延d，也是極小的，被設(shè)置到30 s， 通過查控制nfgpc的m的作用來表現(xiàn)， m的價值選擇與相對天際n設(shè)置了對的相對地大價值10. 對于小m，閉環(huán)反應(yīng)是緩慢而合理的。通過發(fā)現(xiàn)是得到形式m= 6。因此，通過改善，當(dāng)進(jìn)一步增加m， nfgpc以后被用于控制實際能源廠。 級聯(lián)控制計劃的用途是廣泛控制過熱的蒸汽溫度。 前饋控制，與蒸汽流動，并且作為輸入的氣體溫度，可以是應(yīng)用的提供對大變化的一個更加快速的反應(yīng)在這兩個方面。 實際上，前饋道路僅僅是被激活的，當(dāng)這些可變物上的重大的變化時，這些控制方案也防止的更加快速的動力學(xué)植物，即，蒸汽水的閥門和

12、混合的水或者的蒸汽， 影響植物，即，更加緩慢的動力學(xué)的高溫過熱裝置。 全球性非線性nfn模型在表1提出的nfgpc計劃是在下面的圖4。 圖四nfgpc控制過熱蒸汽溫度與前饋控制在nfn里面只暗示二個地方控制器激活了每次，確定由裝載信號通過會員資格作用。 考慮負(fù)載變動，其中裝載從140mw增加到在1%和2.5%/min.之間的速率195兆瓦20分鐘和減退到160兆瓦每60分鐘. 從fig.5，過熱蒸汽的控制溫度由nfgpc達(dá)到，作為溫度兩個的f向上和向下裝載。改變在±7c.之內(nèi)。 這個結(jié)果與比較下的結(jié)果在一個380兆瓦單位被測試，中止干擾。 相反，在波動過熱蒸汽溫度使用是大幅度運用pi

13、控制器，如fig.6所顯示。圖五nfgpc過熱蒸汽溫度控制圖六過熱蒸汽溫度控制的梯級pi控制器 作進(jìn)一步例證，能源廠被模仿在表給的nfn模型1，和是受控的由nfgpc，常規(guī)線性gpc的控制和落下的pi控制器，當(dāng)裝載改變160mw到200mw。 常規(guī)線性gpc控制器是為設(shè)計的地方控制器“medium”操作區(qū)域。 結(jié)果在fig.7， 顯示表里，正如所料，最佳的表現(xiàn)得到nfgpc的方式，因為它被設(shè)計根據(jù)一更加準(zhǔn)確模型。 這由常規(guī)線性跟隨gpc控制器。 常規(guī)的表現(xiàn)pi控制器是最壞的，表明它無法令人滿意的控制過熱蒸汽溫度下大負(fù)載變動。 這也許是控制的原因。 圖七比較研究nfgpc ，傳統(tǒng)的線性gpc的，

14、和級聯(lián)pi控制器 實際上，控制u (t) 的gpc通常是通過計算 (8)得到的。 然而，如果u (t)超出物理極限控制器，控制器飽和發(fā)生。 限制由二次規(guī)劃算法的被合并優(yōu)選的價值函數(shù)受作動器極限支配的 gpcs。 讓在上的變化蒸汽水閥門的頻率被限制： 考慮到nfgpc的表現(xiàn)限制給(18)為裝載干擾40兆瓦在圖8顯示，蒸汽水控制正常0,1。 這個拘束的控制信號nfgpc似乎是有期望作控制器極限。 當(dāng)u超出限制，新的控制信號得到，期望控制會超出極限。 圖八比較限制，優(yōu)化nfgpc和制約因素有限nfgpc表現(xiàn)5 結(jié)論通過一個200mw電廠的塑造和控制使用的神經(jīng)模糊的方法在本文被提出。nfn包括五個地方

15、模型。 網(wǎng)絡(luò)產(chǎn)品是本機(jī)模型插值法b-多槽軸依據(jù)作用給的會員資格。nfgpc同樣地被修建，是被設(shè)計的在nfn的carima模型。 nfgpc適用于與光滑的非線形性的過程它充分的操作范圍可以被分成幾個線性操作地區(qū)。 提出的nfgpc因此為控制提供一個有用的選擇非線性電廠，以前使用傳統(tǒng)方法是受控的，比較困難的。 journal of control theory and applications 2007 5 (1) 83-88 doi 10.1007/s11768-005-5258-6 neuro-fuzzy generalized predictive control of boiler ste

16、am temperature xiangjie liu 1, jizhen liu 1, ping guan 2 ( 1.department of automation, north china electric power university , beijing 102206, china; 2.department of automation, beijing institute of machinery, beijing 100085, china) abstract: power plants are nonlinear and uncertain complex systems.

17、 reliable control of superheated steam temper- ature is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant. a nonlinear generalized predictive controller based on neuro-fuzzy network (nfgpc) is proposed in this paper. the proposed nonlinear

18、controller is applied to control the superheated steam temperature of a 200mw power plant. from the experiments on the plant and the simulation of the plant, much better performance than the traditional controller is obtained. keywords: neuro-fuzzy networks; generalized predictive control; superheat

19、ed steam temperature 1 introduction continuous process in power plant and power station are complex systems characterized by nonlinearity, uncertainty and load disturbance 1, 2. the superheater is an important part of the steam generation process in the boiler-turbine system, where steam is superhea

20、ted before entering the turbine that drives the generator. controlling superheated steam temperature is not only technically challenging, but also economically important 3. from fig.1, the steam generated from the boiler drum passes through the low-temperature superheater before it enters the radian

21、t-type platen superheater. water is sprayed onto the steam to control the superheated steam temperature in both the low and high temperature superheaters. proper control of the superheated steam temperature is extremely important to ensure the overall efficiency and safety of the power plant. it is

22、undesirable that the steam temperature is too high, as it can damage the superheater and the high pres- sure turbine, or too low, as it will lower the efficiency of the power plant. it is also important to reduce the temperature uctuations inside the superheater, as it helps to minimize mechanical s

23、tress that causes micro-cracks in the unit, in or- der to prolong the life of the unit and to reduce maintenance costs. as the gpc is derived by minimizing these uctua- tions, it is amongst the controllers that are most suitable for achieving this goal. the multivariable multi-step adaptive regulato

24、r has been applied to control the superheated steam temperature in a 150 t/h boiler 3, and generalized predictive control was received 14 october 2005; revised 14 october 2006. proposed to control the steam temperature 4. a nonlinear long-range predictive controller based on neural networks is devel

25、oped in 5 to control the main steam temperature and pressure, and the reheated steam temperature at sev- eral operating levels. the control of the main steam pressure and temperature based on a nonlinear model that consists of nonlinear static constants and linear dynamics is presented in 6. fig. 1

26、the boiler and superheater steam generation process. fuzzy logic is capable of incorporating human experi- ences via the fuzzy rules. nevertheless, the design of fuzzy logic controllers is somehow time consuming, as the fuzzy rules are often obtained by trials and errors. in contrast, neu- ral netwo

27、rks not only have the ability to approximate non- linear functions with arbitrary accuracy, they can also be trained from experimental data. the neuro-fuzzy networks (nfns) developed recently have the advantages of model transparency of fuzzy logic, and learning capability of neu- ral networks 7. th

28、e nfns have been used to develop self- this work was supported by the natural science foundation of beijing (no. 4062030), national natural science foundation of china (no. 50576022, 69804003), scientific research common program of beijing municipal commission of education (km200611232007). 84 x. li

29、u et al. / journal of control theory and applications 2007 5 (1) 83-88 tuning control 8, 9, and is therefore a useful tool for de- veloping nonlinear predictive control. since nfns can be considered as a network that consists of several local re- gions, each of which contains a local linear model, n

30、onlin- ear predictive control based on nfns can be devised with the network incorporating all the local generalized predic- tive controllers (gpc) designed using the respective local linear models. following this approach, the nonlinear gener- alized predictive controllers based on the nfns, or simp

31、ly, the neuro-fuzzy generalized predictive controllers (nfg- pcs) are derived here. the proposed controller is then ap- plied to control the superheated steam temperature of the 200mw power unit. experimental data obtained from the plant are used to train the nfn model, and from which lo- cal gpcs t

32、hat form part of the nfgpc is then designed. the proposed controller is tested first on the simulation of the process, before applying it to control the power plant. 2 neuro-fuzzy network modelling consider the following general single-input single-output form a partition of unity, i.e., j j mk (x)

33、º 1, x Î xmin , xmax . and fourth, the output of the basis functions can be obtained by a recurrence equation. nonlinear dynamic system: y (t) = f y(t - 1), ··· , y(t - n y), u(t - d), ··· , u (t - d - n u + 1), e(t - 1), ··· , e(t - n ) +e(t)/d

34、, e fig. 2 neuro-fuzzy network. the membership functions of the fuzzy variables derived from the fuzzy rules can be obtained by the tensor product of the univariate basis functions. as an example, consider the nfn shown in fig.2, which consists of the following fuzzy rules: if operating condition i

35、(x 1 is positive small, ··· , and xn is negative large), then the output is given by the local carima model i: yi (t) = ¯ia y (t - 1) + ··· + ¯a 1 i in¯a yi (t - n ¯a) (1) + i0b du i(t - d) + ··· + binb +e i(t) + ··· + c

36、inc du i(t - d - n b) where f . is a smooth nonlinear function such that a tay- lor series expansion exists, e(t) is a zero mean white noise and d is the differencing operator, n y_, n _ , n _ and d are re- spectively the known orders and time delay of the system. let the local linear model of the n

37、onlinear system (1) at the operating point o(t) be given by the following controlled auto-regressive integrated moving average (carima) model: u e ¯(z_ -1 )y(t) = z -d b (z -1 )du(t) + c(z- 1)e(t), (2) e (it - n c) or ¯i戀 (z -1 )yi (t) = z -d db i(z -1 )u i(t) + c i(z -1 )e i(t), (3) where

38、 a ¯ (z-1 i ) , b i(z -1 ) and c i(z -1 ) are polynomials in the backward shift operator z-1 , and d is the dead time of the plant, u i(t) is the control, and e i(t) is a zero mean inde- pendent random variable with a variance of s 2. the multi- variate basis function a i(x k) is obtained by th

39、e tensor prod- ucts of the univariate basis functions, n k=1 ai = maki (x k), for i = 1, 2, ··· , p, (4) where a¯(z- )1 nomials in z-1 = da(z-1 , ) b(z-1 ) and c(z-1 ) are poly- , the backward shift operator. note that the coefficients of these polynomials are a function of the o

40、p- erating point o(t). the nonlinear system (1) is partitioned into several operating regions, such that each region can be approximated by a local linear model. since nfns is a class of associative memory networks with knowledge stored lo- cally 7, they can be applied to model this class of nonlin-

41、 ear systems. a schematic diagram of the nfn is shown in fig.2. b-spline functions are used as the membership func- tions in the nfns for the following reasons. first, b-spline functions can be readily specified by the order of the basis function and the number of inner knots. second, they are defin

42、ed on a bounded support, and the output of the basis j function is always positive, i.e., m k(x) = 0, x Î / l j and m (x) > 0, x Î l k j-k j-k , l j , l j. third, the basis functions where n is the dimension of the input vector x, and p, the total number of weights in the nfn, is given

43、by, n k=1 p = (r i + k i), (5) where k i and r i are the order of the basis function and the number of inner knots respectively. the properties of the univariate b-spline basis functions described previously also apply to the multivariate basis functions, which is de- fined on the hyper-rectangles.

44、the output of the nfn is, p yi ai y = i=1 p i=1 = yi a i. (6) ai p i=1 x. liu et al. / journal of control theory and applications 2007 5 (1) 83-88 neuro-fuzzy network generalized predic- tive control the gpc is obtained by minimizing the following cost n 85 3 = e p i=1 p i 2 _ j=d (a ) q j yi (t + j

45、) - y r(t + j)2 i function 10, j = e n i=1 2 _ j=1 m + p (a ) l jdu i(t + j - 1)2 = j y r (a i) j i, 2 q (t + j) - y (t + j)2 (11) j=d m l du(t + j - 1) 2, j i=1 where + (7) = e j=1 ji n j=d q j yi (t + j) - y r(t + j) 2 l jdu i(t + j - 1) . 2 where q j and l j are respectively the weighting factors

46、 for the prediction error and the control, y (t + j) is the jth r step ahead reference trajectory, d is the minimum costing horizon, n and m are respectively the maximum costing horizon for the prediction error and the control. the con- trol computed from the nfgpc is the weighted sum of the control

47、 obtained from p local gpc controllers: p i=1 du(t) = a idu i(t), (8) + m j=1 equation (11) shows that minimizing ji (12) is essentially the same as that of minimizing j . from (12), a set of local gen- eralized predictive controllers is obtained, which forms part of the nfgpc. the local gpc 10 is g

48、iven by, du (t) = (g tq g + l ) i i i i i -1 g itq iy r(t + 1) -f idu i(t - 1) - s i(z -1 )y i(t), (13) where yr (t + 1) = ry (t + 1), ry (t + 2), ··· , ry (t + n ) t, du i(t) = du i(t), du i(t + 1), ··· , du i(t + m - 1) t, du i(t - 1) = du i(t - n b), du i(t - n b + 1

49、), ·· , du i(t - 1) , t ) , ··· , sin si si (z -1 ) = s 1i( z -1 ) ,s 2i( z -1 (z -1 t ) . where du i(t) is the control in the ith region, a i(x) is defined previously in (4). note that the weights in the nfgpc are identical to that in the nfn that models the process. since

50、switching between local gpc controllers in the nfgpc involves fuzzy logics, it can be interpreted not only as a fuzzy controller, but also as a fuzzy supervisor. the control can be smooth if the weights a i(x) are suitably selected. from the nfn (6) and the control (8), j given by (7) can be rewritten as: n j=d m j = e q j p i=1 p i=1 a