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. .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 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 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

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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 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 application 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 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.

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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 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 [7], which is discusses the problems in \domain\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 doesn’t meet 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 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 5-th order

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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:

(1)

where

denotes synchronous angle frequency, and

is rotate speed.

are stator’s current, and (d,q)axis.

is number of poles.

are rotor’s flux linkage in is mutual inductance, and

is

rotor’s inductance. J is moment of inertia.and

is rotor’s time constant,

is loadynchronous angle frequency 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

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