外文翻译PLC变频调速的网络反馈系统的实现.docx

1、外文翻译PLC变频调速的网络反馈系统的实现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. .Ho

2、wever, 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 pr

3、oved. 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 adjustor is designed to get high performance. Using PLC, a neural network inverse system can be realized in ac

4、tural 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 tec

5、hnology 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

6、 system. Because of terrible environment and severe disturbance in industrial field, the choice of controller is an important problem. In reference 123, Neural network inverse control was realized by using industrial control computer and several data acquisition cards. The advantages of industrial c

7、ontrol 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 d

8、esigned 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 comp

9、osed 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 45 is a novel control meth

10、od 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 lin

11、ear 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 nonlinear system can realize using this method.Combining the neural network inverse into PLC can easi

12、ly 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 into practice to achieve it full economic and social benefitsIn this paper, firstly the neural network inverse system method is introdu

13、ced, 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 ex

14、periments, and compared with traditional PI control and NN-inverse control.2.Neural Network Inverse System Control MethodThe basic idea of inverse control method 6 is that: for a given system, an-th integral inverse system of the original system is created by feedback method, and combining the inver

15、se 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 mathematic model of the variable performance

16、.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 compl

17、ex system, and desired strict analytical inverse is very obtain, even impossible. The engineering application of inverse system control doesnt meet the expectations. As neural network has non-linear approximate ability, especially for nonlinear complexity system, it becomes with the powerful expecta

18、tions 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

19、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. Mathemat

20、ic Model of Induction Motor Variable FrequencySpeed-Regulating System and Its ReversibilityInduction motor variable frequency speed-regulating system 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 s

21、implified as a 3-order nonlinear model. If the delay of inverter is neglected system original system, the model is expressed as follows: (1)where 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 indu

22、ctance, and is rotors inductance. J is moment of inertia.is rotors time constant, and is loadynchronous angle frequency torque.In vector mode, thenSubstituted it into formula (1), then (2)Taking reversibility analyses of forum (2), thenThe state variables are chosen as followsInput variables areTaki

23、ng 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 (con

24、sidering 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 connecte

25、d 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 System4.1 Acquisition of the Input and Output Training Samples Training samples are extremely important in the reconstruction of neural network inverse sys

26、tem. 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

27、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 should include all training s

28、amples 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. T

29、he 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 refer

30、ence 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 and a dynamic neural network composed of original system is obtained.5 .Experiments and Results5.1 Hardware of the System The hardw

31、are 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 installed with motor and Control photoelectric coder.PLC controller chooses S7-315-2DP, w

32、hich has a PROFIBUS-DP interface and a MPI interface. Speed acquisition module is FM350-1. WinCC is connected with the experiment system S7-300 by CP5611 using MPI protocol.The type of inverter is MMV of SIEMENS. It can communicate with SIEMENS PLC by USS protocol. ACB15 module is added on the inverter in this system.5.2 Software Program5.2.1 Communication IntroductionMPI (MultiPoint Interface) is a simple and inexpensive communication strategy using in slowly and non-large data transforming field. The d