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.controller翻译中文
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 syst em 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. Subs
equently, 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 n e g l e c t e d, the model is expressed as follows:
(1)
where denotes synchronous angle frequency, and is rotate speed. are stator’s
c u r r e n t,a n
d a r
e r o t o r’s
f l u x l i n k a
g e i n
(d,q)axis.is number of poles. is mutual inductance, and is rotor’s inductance. J is
m o m e n t o f i n e r t i a.i s r o t o r’s t i m e c o n s t a n t,a n d
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, 1600g r o u p s 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 la
yer, 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, c l o s e-l o o p,f i r s t o r d e r 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 i n v e r s e m o d e l o f o r i g i n a l 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 b
y, 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

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