【機械類畢業(yè)論文中英文對照文獻翻譯】PLC變頻調(diào)速的網(wǎng)絡(luò)反饋系統(tǒng)的實現(xiàn)【word英文1937字6頁word中文翻譯3364字5頁】
【機械類畢業(yè)論文中英文對照文獻翻譯】PLC變頻調(diào)速的網(wǎng)絡(luò)反饋系統(tǒng)的實現(xiàn)【word英文1937字6頁word中文翻譯3364字5頁】,機械類畢業(yè)論文中英文對照文獻翻譯,word英文1937字6頁,word中文翻譯3364字5頁,機械類,畢業(yè)論文,中英文,對照,對比,比照,文獻,翻譯,plc,變頻,調(diào)速,網(wǎng)絡(luò),反饋,系統(tǒng),實現(xiàn),word
中文譯文
PLC變頻調(diào)速的網(wǎng)絡(luò)反饋系統(tǒng)的實現(xiàn)
摘要。變頻調(diào)速系統(tǒng),包括一個異步電動機和通用逆變器、且PLC控制被廣泛地應(yīng)用于工業(yè)領(lǐng)域。然而,對多變量、非線性和強耦合的異步電機的控制性能卻不足,不能很好地滿足客戶的調(diào)速要求。該數(shù)學(xué)模型的變頻調(diào)速系統(tǒng)提出了矢量控制方式,其可逆轉(zhuǎn)性得到證實。通過構(gòu)建一種基于神經(jīng)網(wǎng)絡(luò)的逆系統(tǒng),并結(jié)合變頻調(diào)速系統(tǒng),pseudo-linear系統(tǒng)被完成了,并且為了得到性能優(yōu)良的系統(tǒng)采用了一個線性閉環(huán)調(diào)節(jié)器。采用PLC、神經(jīng)網(wǎng)絡(luò)逆系統(tǒng)在實際系統(tǒng)可以實現(xiàn)。實驗結(jié)果表明變頻調(diào)速系統(tǒng)的性能得到了很大的提高，并且神經(jīng)網(wǎng)絡(luò)反饋控制的可行性得到了驗證。
1. 導(dǎo)論
近年來,隨著電力電子技術(shù)、微電子技術(shù)和現(xiàn)代控制理論,逐漸涉及到交流電機系統(tǒng),這些技術(shù)已經(jīng)廣泛應(yīng)用于變頻器調(diào)速的AC馬達。變頻調(diào)速系統(tǒng),包括一個異步電動機和通用逆變器，用來代替直流調(diào)速系統(tǒng)。由于在工業(yè)領(lǐng)域中的糟糕的環(huán)境和嚴重的干擾,選擇控制器是一個十分重要的問題。在文獻[1][2][3],介紹了利用工業(yè)控制計算機和數(shù)據(jù)采集卡實現(xiàn)了神經(jīng)網(wǎng)絡(luò)反饋控制。工業(yè)控制計算機的優(yōu)勢有較高的計算速度，龐大的記憶能力以及與其他軟件良好的兼容性等。但是工業(yè)控制計算機在工業(yè)應(yīng)用上也有一些不足，比如運行不穩(wěn)定,不可靠及更惡劣的通信能力?？删幊绦蚩刂破?PLC)控制系統(tǒng)是專為工業(yè)環(huán)境中的應(yīng)用而設(shè)計的,它的穩(wěn)定性和可靠性好。PLC控制系統(tǒng),可以很容易地集成到現(xiàn)場總線控制系統(tǒng)并得到高性能的通信結(jié)構(gòu),所以它在近年來被廣泛地使用,并且深受歡迎。該系統(tǒng)由普通的逆變器和異步電機組成，是一種復(fù)雜的非線性系統(tǒng),傳統(tǒng)的PID控制策略,并不能滿足要求和進一步控制。因此,如何加強系統(tǒng)的控制性能是非常迫切的事情。
神經(jīng)網(wǎng)絡(luò)逆系統(tǒng)[4][5], 在未來幾年里將是一種新型的控制方法。其基本的想法是:對于一個給定的系統(tǒng),原系統(tǒng)的逆系統(tǒng)是由一個動態(tài)神經(jīng)網(wǎng)絡(luò)引起的,對象信號和反饋信號的組合系統(tǒng)被轉(zhuǎn)化成一種線性關(guān)系的解耦標準系統(tǒng)。隨后,一個線性閉環(huán)調(diào)節(jié)器設(shè)計可以達到較高的控制性能。該方法的優(yōu)點是在工程上很容易實現(xiàn)。在線性化及其解耦控制正常的非線性系統(tǒng)能實現(xiàn)采用這種方法。
把神經(jīng)網(wǎng)絡(luò)反饋結(jié)合到可編程序控制器(PLC)上就可以很容易地彌補不足的問題，解決在PLC控制系統(tǒng)上的非線性耦合。這個組合可以促進神經(jīng)網(wǎng)絡(luò)反饋付諸實踐，來實現(xiàn)其全部的經(jīng)濟效益和社會效益。
在這篇文章中，首先對神經(jīng)網(wǎng)絡(luò)反饋方法進行了介紹,并且描述了采用矢量控制的變頻調(diào)速系統(tǒng)的數(shù)學(xué)模型。然后是對反饋系統(tǒng)進行分析的的介紹,并給出了關(guān)于PLC控制系統(tǒng)中構(gòu)造NN-反饋系統(tǒng)的方法和步驟。最后,該方法在實驗中被驗證,并將傳統(tǒng)的PI控制和NN-反饋控制進行了對比。
2. 神經(jīng)反饋網(wǎng)絡(luò)控制方法
基本的反饋控制方法[6]就是:對于一個給定的系統(tǒng)、一種α-th由反饋方法建立的完整的反饋系統(tǒng),并結(jié)合反饋系統(tǒng)與原系統(tǒng)的特點,提出了一種解耦的線性關(guān)系,以標準化體系,并命名為偽線性系統(tǒng)。隨后,一個線性閉環(huán)調(diào)節(jié)器運行并將達到較高的控制性能。
當在“幾何領(lǐng)域”討論這些問題時，反饋系統(tǒng)控制方法并不像微分幾何方法，其特點是直接，簡單，易于理解。主要的問題是怎樣在應(yīng)用軟件中獲得反饋模型。由于非線性系統(tǒng)是一個復(fù)雜的系統(tǒng),所以很難要求嚴格解析反饋信號,這甚至是不可能的。反饋系統(tǒng)控制在工程應(yīng)用中不能達到期望值。作為神經(jīng)網(wǎng)絡(luò)非線性逼近能力,尤其是對于非線性的復(fù)雜系統(tǒng),它會是來解決問題的強大工具。反饋系統(tǒng)集成了具有非線性逼近能力的反饋系統(tǒng)，其中具有非線性逼近能力的反饋系統(tǒng)能夠避免使用反饋方法帶來的麻煩。這樣就可能，運用反饋控制方法去控制一個復(fù)雜的非線性系統(tǒng)。a ? th NN 反饋系統(tǒng)的控制方法只需要較少的系統(tǒng)信息，比如與系統(tǒng)相關(guān)的命令，并且容易獲得運行網(wǎng)絡(luò)的反饋模型。原系統(tǒng)的層疊式的 NN反饋系統(tǒng),會形成一個偽線性系統(tǒng)。然后,一個線性閉環(huán)調(diào)節(jié)校準器將工作。
3. 異步電機變頻調(diào)速系統(tǒng)的數(shù)學(xué)模型和它的反饋性能
異步電機變頻調(diào)速系統(tǒng)提供的跟蹤電流正弦脈寬調(diào)制逆變器可以表示為非線性模型在兩相循環(huán)的協(xié)調(diào)。該模型簡化為一個3-order非線性模型。如果忽略逆變器的延遲,該模型表述如下:
（1）
（表示同步角頻率；表示轉(zhuǎn)速；
表示定子的電流；表示轉(zhuǎn)子在（qd）軸線上的不穩(wěn)定部分；
表示點的數(shù)量；表示互感系數(shù)；表示慣性轉(zhuǎn)矩；
表示轉(zhuǎn)子的時間常數(shù)；表示負載轉(zhuǎn)矩。）
用矢量模式,得
代進公式(1),得
（2）
可逆轉(zhuǎn)性分析(2),得
（3） （4）
可供選擇的狀態(tài)變量如下
輸入變量
由公式(4)得出結(jié)果,得
（5）
（6）
然后雅可比矩陣
（7）
（8）
作為 所以并且系統(tǒng)是可逆的。
相關(guān)的系統(tǒng)是
當變頻器運行模式的變化,在矢量磁鏈的可以忽略的磁鏈(考慮到是恒定,等于等級)。原系統(tǒng)簡化為一個輸入和輸出系統(tǒng)訂立的(2)。
根據(jù)隱函數(shù)定理，公式(3)的反饋系統(tǒng)可以表達為:
（9）
當反饋系統(tǒng)連續(xù)連接到原系統(tǒng)時，偽線性復(fù)合系統(tǒng)形成類型。
4. 網(wǎng)絡(luò)反饋系統(tǒng)的實現(xiàn)步驟
4.1 輸入與輸出的運行樣本的采集
采樣對網(wǎng)絡(luò)反饋系統(tǒng)的建立是極其重要的。它不僅需要獲得原系統(tǒng)的動態(tài)數(shù)據(jù),還需要獲得了靜態(tài)的數(shù)據(jù)。參考信號應(yīng)該包括原始系統(tǒng)所有的工作范圍,并確保近似。信號的欲處理的第一階段是從每0HZ到50HZ中得到10HZ，并得到開環(huán)響應(yīng)。第二階段是混亂信號的輸入，當每10秒鐘出現(xiàn)預(yù)處理信號時，隨機信號輸入,并得到閉環(huán)響應(yīng)?；谶@些輸入,將得到1600組得到運行樣本。
4.2 網(wǎng)絡(luò)的建設(shè)
靜態(tài)神經(jīng)網(wǎng)絡(luò)和動態(tài)神經(jīng)網(wǎng)絡(luò)的完美組合將能構(gòu)建一個反饋系統(tǒng)。靜態(tài)神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)是由2個輸入層的神經(jīng)元，3個輸出層的神經(jīng)元和12個隱蔽層的神經(jīng)元組成。隱藏神經(jīng)元的激勵函數(shù)是單調(diào)平滑雙曲正切函數(shù)。輸出層是由線性臨界激勵函數(shù)的神經(jīng)元組成。運行數(shù)據(jù)是這些速度的開環(huán)，閉環(huán)的相對應(yīng)速度和設(shè)置的參考的速度。50次運行之后，神經(jīng)網(wǎng)絡(luò)的運行錯誤達到0.001。神經(jīng)網(wǎng)絡(luò)的負荷和臨界值被保存下來。并得到原系統(tǒng)的反饋模型。
5. 實驗和結(jié)果
5.1 系統(tǒng)硬件
硬件系統(tǒng)包括上層監(jiān)督計算機安裝，控制結(jié)構(gòu)軟件WinCC6.0,西門子S7-300PLC，變頻器，異步電動機和光電編碼器。
選擇S7-315-2DP PLC控制器,它有一個PROFIBUS-DP接口和一個MPI接口。高速采集模塊是FM350-1。WinCC用MPI協(xié)議被CP5611貫穿到S7-300。
這個逆變器的類型是西門子的MMV。西門子的PLC能兼容美國的協(xié)議。在這個系統(tǒng)上ACB15模塊被增加在逆變器上。
5.2 軟件編程
5.2.1 通信介紹
MPI(多點接口)是一種簡單、便宜的通訊策略，運用在運行慢，非大型數(shù)據(jù)轉(zhuǎn)換的場合。在WinCC與PLC之間的數(shù)據(jù)轉(zhuǎn)換不是很大,所以選擇MPI協(xié)議。
MMV變頻器作為從動裝置連接到PROFIBUS網(wǎng)絡(luò),并安裝到CB15 PROFIBUS模塊上。PPO1或PPO3的數(shù)據(jù)類型可供選擇。它允許控制信號直接發(fā)送到變頻地址,或者使用STEP7V5.2 SFC14/15的系統(tǒng)功能模塊。
OPC能有效的提供完整的數(shù)據(jù)和通信能力。不同類型的服務(wù)器和客戶機可以存取彼此的數(shù)據(jù)來源。比較傳統(tǒng)的軟件模式和硬件發(fā)展,設(shè)備生產(chǎn)商只需要培養(yǎng)一個操作員。這樣可以縮短開發(fā)周期,節(jié)省人力資源,并簡化了整個控制系統(tǒng)的結(jié)構(gòu)。
矩陣實驗室的神經(jīng)網(wǎng)絡(luò)運行需要系統(tǒng)各種各樣數(shù)據(jù)的時候，這些數(shù)據(jù)不能從PLC或WinCC直接讀取。所以O(shè)PC技術(shù)可以用來獲得在WinCC和Exce之中所需的數(shù)據(jù)。設(shè)置WinCC作為OPC DA的服務(wù)器,一個OPC客戶將被很好的建立關(guān)于VBA。系統(tǒng)的實時數(shù)據(jù)被WinCC讀取并寫到Excel上,然后Excel上的數(shù)據(jù)被轉(zhuǎn)換到矩陣實驗室為在離線運行時獲得原系統(tǒng)的反饋系統(tǒng)。
5.2.2控制程序
通常用STEP7 V5.2的標準模板庫來對通訊，數(shù)據(jù)采集和控制算法進行編程，速度采樣程序和存儲程序被編程為有規(guī)律的中斷程序A，中斷周期為100毫秒。為了阻止程序A運行時間超過100毫秒，減小程序的運行周期和系統(tǒng)錯誤，控制步驟和神經(jīng)網(wǎng)絡(luò)算法被編程為主程序B。
神經(jīng)網(wǎng)絡(luò)算法標準化對運行采樣來說是必要的以便加快信號收集速度，在最終運行之前輸入和輸出信號乘以一個放大系數(shù)。
5.3 實驗結(jié)果
當速度參照是100秒每周期的方波信號時,逆變器運行的是矢量模式。結(jié)果表明,神經(jīng)網(wǎng)絡(luò)控制的跟蹤性能均優(yōu)于傳統(tǒng)的常規(guī)PI控制。
當速度參照保持恒定時,經(jīng)過80秒時間，負荷降低到?jīng)]有負荷,經(jīng)過120秒時間,負荷增加到滿負荷，所以在傳統(tǒng)控制下的速度響應(yīng)曲線和網(wǎng)絡(luò)反饋控制下的速度響應(yīng)曲線如下圖所示。很明顯,在穩(wěn)定性能上，網(wǎng)絡(luò)反饋控制的負載擾動優(yōu)于傳統(tǒng)的PI控制的負載擾動。
（PI控制下的速度響應(yīng)） （網(wǎng)絡(luò)反饋控制下的速度響應(yīng)）
6. 結(jié)論
為了改善PLC變頻調(diào)速系統(tǒng)的控制性能，因而神經(jīng)網(wǎng)絡(luò)反饋系統(tǒng)被使用。并給出了一個變頻調(diào)速系統(tǒng)的數(shù)學(xué)模型,且其可逆轉(zhuǎn)性得到了檢驗。反饋系統(tǒng)和原系統(tǒng)被組合并構(gòu)建成偽線性系統(tǒng)，并設(shè)計了線性控制的方法進行控制。通過實驗,PLC神經(jīng)網(wǎng)絡(luò)的反饋系統(tǒng)在工業(yè)應(yīng)用中具有有效性和可行性的到了驗證。
英文原文：
Realization of Neural Network Inverse System with PLC in Variable Frequency Speed-Regulating System
Abstract. The variable frequency speed-regulating system which consists of an induction motor and a general inverter, and controlled by PLC is widely used in industrial field. .However, for the multivariable, nonlinear and strongly coupled induction motor, the control performance is not good enough to meet the needs of speed-regulating. The mathematic model of the variable frequency speed-regulating system in vector control mode is presented and its reversibility has been proved. By constructing a neural network inverse system and combining it with the variable frequency speed-regulating system, a pseudo-linear system is completed, and then a linear close-loop is designed to get high performance. Using PLC, a neural network inverse system can be realized in system. The results of experiments have shown that the performances of variable frequency speed-regulating system can be improved greatly and the practicability of neural network inverse control was testified.
1.Introduction
In recent years, with power electronic technology, microelectronic technology and modern control theory infiltrating into AC electric driving system, inverters have been widely used in speed-regulating of AC motor. The variable frequency speed-regulating system which consists of an induction motor and a general inverter is used to take the place of DC speed-regulating system. Because of terrible environment and severe disturbance in industrial field, the choice of controller is an important problem. In reference [1][2][3], Neural network inverse control was realized by using industrial control computer and several data acquisition cards. The advantages of industrial control computer are high computation speed, great memory capacity and good compatibility with other software etc. But industrial control computer also has some disadvantages in industrial application such as instability and fallibility and worse communication ability. PLC control system is special designed for industrial environment application, and its stability and reliability are good. PLC control system can be easily integrated into field bus control system with the high ability of communication configuration, so it is wildly used in recent years, and deeply welcomed. Since the system composed of normal inverter and induction motor is a complicated nonlinear system, traditional PID control strategy could not meet the requirement for further control. Therefore, how to enhance control performance of this system is very urgent.
The neural network inverse system [4][5] is a novel control method in recent years. The basic idea is that: for a given system, an inverse system of the original system is created by a dynamic neural network, and the combination system of inverse and object is transformed into a kind of decoupling standardized system with linear relationship. Subsequently, a linear close-loop regulator can be designed to achieve high control performance. The advantage of this method is easily to be realized in engineering. The linearization and decoupling control of normal
system can realize using this method.
Combining the neural network inverse into PLC can easily make up the insufficiency of solving the problems of nonlinear and coupling in PLC control system. This combination can promote the application of neural network inverse into practice to achieve its full economic .
In this paper, firstly the neural network inverse system method is introduced, and mathematic model of the variable frequency speed-regulating system in vector control mode is presented. Then a reversible analysis of the system is performed, and the methods and steps are given in constructing NN-inverse system with PLC control system. Finally, the method is verified in
traditional PI control and NN-inverse control.
2.Neural Network Inverse System Control Method
The basic idea of inverse control method [6] is that: for a given system, anα-th integral inverse system of the original system is created by feedback method, and combining the inverse system with original system, a kind of decoupling standardized system with linear relationship is obtained, which is named as a pseudo linear system as shown in Fig.1. Subsequently, a linear close-loop regulator will be designed to achieve high control performance.
Inverse system control method with the features of direct, simple and easy to understand does not like differential geometry method [7], which is discusses the problems in "geometry domain". The main problem is the acquisition of the inverse model in the applications. Since non-linear system is a complex system, and desired strict inverse is very difficult to
obtain, even impossible. The engineering application of inverse system control don’t meet the expectations. As neural network has non-linear approximate ability, especially for nonlinear
the powerful tool to solve the problem.
a ? th NN inverse system integrated inverse system with non-linear ability of the neural network can avoid the troubles of inverse system method. Then it is possible to apply inverse control method to a complicated non-linear system. a ? th NN inverse system method needs less system information such as the relative order of system, and it is easy to obtain the inverse model by neural network training. Cascading the NN inverse system with the original system, a pseudo-linear system is completed. Subsequently, a linear close-loop regulator will be designed.
3. Mathematic Model of Induction Motor Variable Frequency
Speed-Regulating System and Its Reversibility
Induction motor variable frequency speed-regulating system supplied by the inverter of tracking current SPWM can be expressed by 5th order nonlinear model in d-q two-phase rotating coordinate. The model was simplified as a 3-order nonlinear model. If the delay of inverter is neglected,
the model is expressed as follows:
(1)
where denotes synchronous angle frequency, and is rotate speed. are stator’s current, and are rotor’s flux linkage in
(d,q)axis. is number of poles. is mutual inductance, and is rotor’s inductance. J is moment of inertia.is rotor’s time constant, and
is load torque.
In vector mode, then
Substituted it into formula (1), then
(2)
Taking reversibility analyses of forum (2), then
The state variables are chosen as follows
Input variables are
Taking the derivative on output in formula(4), then
(5)
(6)
Then the Jacobi matrix is Realization of Neural Network Inverse System with PLC
(7)
(8)
As so and system is reversible. Relative-order of system is
When the inverter is running in vector mode, the variability of flux linkage can be neglected (considering the flux linkage to be invariableness and equal to the rating). The original system was simplified as an input and an output system concluded by forum (2).
According to implicit function ontology theorem, inverse system of formula (3)
can be expressed as
(9)
When the inverse system is connected to the original system in series, the pseudo linear compound system can be built as the type of
4. Realization Steps of Neural Network Inverse System
4.1 Acquisition of the Input and Output Training Samples
Training samples are extremely important in the reconstruction of neural network inverse system. It is not only need to obtain the dynamic data of the original system, but also need to obtain the static date. Reference signal should include all the work region of original system, which can be ensure the approximate ability. Firstly the step of actuating signal is given corresponding every 10 HZ form 0HZ to 50HZ, and the responses of open loop are obtain. Secondly a random tangle signal is input, which is a random signal cascading on the step of actuating signal every 10 seconds, and the close loop responses is obtained. Based on these inputs, 1600 groups
training samples are gotten.
4.2 The Construction of Neural Network
A static neural network and a dynamic neural network composed of integral is used to construct the inverse system. The structure of static neural network is 2 neurons in input layer, 3 neurons in output layer, and 12 neurons in hidden layer. The excitation function of hidden neuron is monotonic smooth hyperbolic tangent function. The output layer is composed of neuron with linear threshold excitation function. The training datum are the corresponding speed of open-loop, close-loop, first order
derivative of these speed, and setting reference speed. After 50 times training, the training error of neural network achieves to 0.001. The weight and threshold of the neural network are saved. The inverse model of original
system is obtained.
5 .Experiments and Results
5.1 Hardware of the System
The hardware of the experiment system is shown in Fig 5. The hardware system includes upper computer installed with Supervisory & Control configuration software WinCC6.0 [8], and S7-300 PLC of SIEMENS, inverter, induction motor and photoelectric coder.
PLC controller chooses S7-315-2DP, which has a PROFIBUS-DP interface and a MPI
is connected with S7-300 by CP5611 using MPI protocol.
The type of inverter is MMV of SIEMENS. It can communicate with SIEMENS PLC by
inverter in this system.
5.2 Software Program
5.2.1 Communication Introduction
MPI (Mu Point Interface) is a simple and inexpensive communication strategy using in slowly and non-large data transforming field. The data transforming between and PLC is not large,
chosen.
The MMV inverter is connected to the PROFIBUS network as a slave station, which is mounted with CB15 PROFIBUS module. PPO1 or PPO3 data type can be chosen. It permits to send the control data directly to the inverter addresses, or to use the system function blocks of
SFC14/15.
OPC can efficiently provide data integral and intercommunication. Different type servers and clients can access data sources of each other. Comparing with the traditional mode of software and hardware development, equipment manufacturers only need to develop one driver. This can short the development cycle, save manpower resources, and simplify the structure
of the entire control system.
Variety data of the system is needed in the neural network training of , which can not obtain by reading from PLC or directly. So OPC technology can be used l to obtain the needed data between . Setting as OPC DA server, an OPC client is constructed in Excel by VBA. System real time data is and to Excel by, and then the data in Excel is transform to for offline
training to get the inverse system of original system.
5.2.2 Control Program
Used STL to program the communication and data acquisition and control algorithm subroutine in STEP7 V5.2, velocity sample subroutine and storage subroutine are programmed in regularly interrupt A, and the interrupt cycle chooses 100ms. In order to minimum the cycle time of A to prevent the run time of A exceeding 100ms and system error, the control procedure and
procedure B.
In neural network algorithm normalized the training samples is need to speed up the rate of n
input and output data before the final training.
5.3 Experiment Results
When speed reference is square wave signal with 100 seconds cycle, where the inverter is
tracking performance of neural network control is better than traditional PI control.
When speed reference keeps in constant, and the load is reduced to no load at 80 seconds, and increased to full load at 120 seconds, the response curves of speed with traditional PI control and neural network inverse control are shown in Fig. 11 and 12 respectively. It is clearly that the performance of resisting the load disturbing with neural network inverse
control is better than the traditional PI control.
(Speed response in PI control)
(Speed response in neural network inverse control)
6. Conclusion
In order to improve the control performance of PLC Variable Frequency Speed-regulating System, neural network inverse system is used. A mathematic model of variable frequency speed-regulating system was given, and its reversibility was testified. The inverse system and original system is compound to construct the pseudo linear system and linear control method is design to control. With experiment, neural network inverse system with PLC has its effectiveness and its feasibility in industry application.
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