PLC变频调速的网络反馈系统的实现外文翻译(定稿)

1、 毕业设计（外文翻译）英文题目 Realization of Neural Network Inverse System with PLC in Variable Frequency Speed-Regulating System 中文题目 PLC变频调速的网络反馈系统的实现系 （院） 自动化系 专 业 电气工程与自动化 学生姓名 00 学 号 2009022221 指导教师 李思光 职 称 讲 师 二一三 年六月Realization of Neural Network Inverse System with PLC in Variable Frequency Speed-Regulatin

2、g System 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

3、 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,

4、 a pseudo-linear system is completed, and then a linear close-loop adjustor is designed to get high performance. Using PLC, a neural network inverse system can be realized in actural system. The results of experiments have shown that the performances of variable frequency speed-regulating system can

5、 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-r

6、egulating 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 import

7、ant problem. 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 com

8、puter 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

9、 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 requireme

10、nt for further control. Therefore, how to enhance control performance of this system is very urgent.The neural network inverse system 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

11、, 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 engineer

12、ing. The linearization and decoupling control of normal nonlinear 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 applicat

13、ion of neural network into practice to achieve it full economic and social benefits.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

14、 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 experiments, and compared with traditional PI control and NN-inverse control.2. Neural Network Inverse System Control MethodThe basic idea of in

15、verse control method is that: for a given system, an-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 linea

16、r system as shown in Fig.1. Subsequently, a linear close-loop regulator will be designed to achieve high control mathematic model of the variable performance.Inverse system control method with the features of direct, simple and easy to understand does not like differential geometry method, which is

17、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 analytical inverse is very obtain, even impossible. The engineering application of inverse system control doesnt me

18、et the expectations. As neural network has non-linear approximate ability, especially for nonlinear complexity system, it becomes with the powerful expectations tool to solve the problem.a th NN inverse system integrated inverse system with non-linear ability of the neural network can avoid the trou

19、bles 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 th

20、e 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 ReversibilityInduction motor variable frequency speed-regulating s

21、ystem supplied by the inverter of tracking current SPWM can be expressed by 5-th 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 system original system, the model is expressed as follows: (3.1)wh

22、ere denotes synchronous angle frequency, and is rotate speed. , are stators current, and , are rotors flux linkage in (d,q)axis. is number of poles. is mutual inductance, and is rotors inductance. is moment of inertia.is rotors time constant, and is loadynchronous angle frequency torque.In vector mo

23、de, then and So Substituted it into formula (3.1), then (3.2)Taking reversibility analyses of forum (3.2), then (3.3) (3.4)The state variables are chosen as followsInput variables areTaking the derivative on output in formula(3.4), then (3.5) (3.6)Then the Jacobi matrix is Realization of Neural Netw

24、ork Inverse System with PLC (3.7) (3.8)As so and system is reversible. Relative-order of system is ，and .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

25、simplified as an input and an output system concluded by forum (3.2).According to implicit function ontology theorem, inverse system of formula (3.3)can be expressed as (3.9)When the inverse system is connected to the original system in series, the pseudo linear compound system can be built as the t

26、ype of .4. Vector Control of Induction MachinesThe derivation of the vector-controlled (VC) method and its application to the induction machine is considered in this section. The vector description of the machine will be derived in the rst subsection, followed by the dynamic model description in the

27、 second subsection. Field-oriented control (FOC) of the induction machine will be presented in the third subsection and the direct torque control (DTC) method will be described in the last subsection.4.1 Vector Formulation of the Induction MachineThe stator and rotor windings for the three-phase ind

28、uction machine are shown in Fig. . The windings are sinusoidally distributed, but are indicated on the gure as point windings. If is the number of turns for each winding, then the winding density distributions as functions of are given by (4.1) whereis the angle around the stator referenced from pha

29、se as-axis. The magnemotive force (MMF) distributions corresponding to () are () FIGURE4.1 Induction machine stator and rotor windingsThese scalar equations can be represented by dot products between the following MMF vectors （） and the unit vector whose angle with the as-axis is . The vectors，and r

30、epresent unit vectors along the respective winding axes. All the machine quantities, including the phase currents and voltages, and ux linkages can be expressed in this vector form.The vectors along the three axes as, bs, and cs do not form an independent basis set. It is convenient to transform thi

31、s basis set to one that is orthogonal, the so-called dq-transformation, originally proposed by R. H. Park for application to the synchronous machine. Figure illustrates the relationship between the degenerate abc and orthogonal qd0 vector sets. If is the angle between and , then the transformation r

32、elating the two coordinate systems can be expressed as （）where ，The variable is called the zero-sequence component and is obtained using the last row in the matrix W . This last row is included to make the matrix invertible, providing a one-to-one transformation between the two coordinate systems. T

33、his row is not needed if the transformation acts on a balance set of variables, because the zero-sequence component is equal to zero. The zero-sequence FIGURE 4.2 Illustration for reference frame transformationcomponent carries information about the neutral the neutral point of the abc variables bei

34、ng transformed. If the set is not balanced, this neutral point is not necessarily zero. The constant multiplying the matrix of () is, in general, arbitrary. With this constant equal to as it is in (), the result is the power invariant transformation. By using this transformation, the calculated powe

35、r in the abc coordinate system is equal to that computed in the qd0 system. If the angle=0, the result is a transformation from the stationary abc system to the stationary qd0 system. However, transformation to a reference frame rotating at an arbitrary speed is possible by dening （）As will be seen

36、later, the rotor uxoriented vector control method makes use of this concept, trans- forming the machine variables to the synchronous reference frame where they are constants in steady state .To understand this concept intuitively, consider the balanced set of stator MMF vectors of a typical inductio

37、n machine given in (). It is not difcult to show that the sum of these vectors produces a resultant MMF vector that rotates at the frequency of the stator currents. The length of the vector is dependent upon the magnitude of the MMF vectors. Observing the system from the synchronous reference frame

38、effectively removes the rotational motion, resulting in only the magnitude of the vector being of consequence. If the magnitudes of the MMF vectors are constant, then the synchronous variables will be constant. Transients in the magnitudes of the stationary variables result in transients in the sync

39、hronous variables. This is true for currents, voltages, and other variables associated with the machine.4.2 Induction Machine Dynamic ModelThe six-state induction machine model in the arbitrary reference frame is presented in this section. This dynamic model will be used to derive the FOC and DTC me

40、thods. As will be seen, the derivations of these control methods will be simpler if they are performed in a specic coordinate reference frame. An additional advantage is that transforming to the qd0 coordinate system in any reference frame removes the time-varying inductances associated with the ind

41、uction machine 10. The machine model in a given reference frame is obtained by substituting the appropriate frequency for in the model equations. The state equations for the six-state induction motor model in the arbitrary reference frame are given in Eqs. () through ().The induction machine nomencl

42、ature is provided in Table . The derivative operator is denoted by p, and the rotor quantities are referred to the stator. The state equations are （） （） （） （） （） （）Table 4.1 Induction Machine NomenclatureThe induction machine nomenclature is provided in Table The derivative operator is denoted by p,

43、 and the rotor quantities are referred to the stator. The state equations arewhere the stator and rotor ux linkages are given by （） （） （） （）The electrical torque developed by the machine is 4, 5 （）where the stator transient reactance is dened as ，whereand .It is important to note that in Eqs. () and

44、 (), the shaft speed is expressed in electrica radians-per-second, that is, scaled by the number of machine pole pairs.4.3 Field-Oriented Control of the Induction MachineField-oriented control is probably the most common control method used for high-performance induction machine applications. Rotor

45、ux orientation (RFO) in the synchronous reference frame is considered here. There are other orientation possibilities, but rotor ux orientation is the most prominent, and so will be presented in detail.The RFO control method involves making the induction machine behave similarly to a DC machine. The

46、 rotor ux is aligned entirely along the d-axis. The stator currents are split into two components: a field-producing component that induces the rotor ux and a torque-producing component that is orthogonal to the rotor eld. This is analogous to the DC machine where the eld ux is along one direction,

47、and the commutator ensures an orthogonal armature current vector. This task is greatly simplied through transformation of the machine variables to the synchronously rotating reference frame. Under FOC, the q-axis rotor ux linkage is zero in the synchronous reference frame, by using Eq. (), the elect

48、ric torque of the induction machine can be expressed as ()where the e superscript indicates evaluation in the synchronous reference frame. This torque equation is very similar to that of the DC machine. If either the ux linkage or current is held constant, then the torque can be controlled by changi

49、ng the other. Assuming the inverter driving the induction machine is current sourced, the stator currents can be controlled almost instantaneously. However, by setting = 0 in Eq. () and substituting the result in Eq. (), it can be shown that the d-axis rotor ux linkage is governed by （）where r is te

50、rmed the rotor time constant. Equation () dictates that the rotor ux cannot be changed arbitrarily fast. Therefore, the best dynamic torque response will result if the rotor ux linkage is held constant, and the electrical torque is controlled by changing Assuming a current-sourced inverter, this con

51、trol conguration allows torque control for which the response is limited only by the response time of the inverter driving the machine. Implementation of RFO control requires that the machine variables be transformed to the synchronous reference frame. To accomplish this task, the synchronous refere

52、nce frame speed must be calculated in some manner. There are two common methods of nding the synchronous speed. In indirect FOC, the synchronous speed is obtained by using a rotor speed measurement and a corresponding slip calculation. Direct FOC uses air-gap ux measurement or other machine-related

53、quantities to compute the synchronous speed. The indirect method is the most common and will be presented here.In indirect FOC, the synchronous reference frame speed must be found, and this value integrated to obtain the angle used in the reference frame transformation. Rewriting Eq. () withyields l

54、qr （）Again, with, rewrite Eq. (11.21) as （）Substitution of Eq. () into Eq. () yields the desired expression for （）This expression provides the needed synchronous speed in terms of the rotor ux, which is specied by the controller, and the q-axis stator current that is adjusted for torque control. The

55、 rotor ux time constant is required for the slip calculation, and in many cases must be estimated online because of its dependence on temperature and other factors. The d-axis stator current needed to produce a given rotor ux can be computed using Eq. (). The angle used for the reference frame trans

56、formation is calculated via （）5. Realization Steps of Neural Network Inverse System5.1 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 reg