1、 1 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 mul
2、tivariable, 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 construct
3、ing 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
4、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 moder
5、n 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
6、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 control computer a
7、re 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 indus
8、trial environment application, and its stability and reliability are good. PLC 2 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
9、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 method in recent y
10、ears. 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
11、 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 th
12、e insufficiency of solving the problems of nonlinear and coupling in PLC control system. This combination can promote the application of neural network into p r a c t i c e t o a c h i e v e i t f u l l e c o n o m i c a n d s o c i a l b e n e f i t s In this paper, firstly the neural network inver
13、se 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, t
14、he method is verified in experiments, and compared with 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, an-th integral inverse system of the original system is created by feedback me
15、thod, 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 3 achieve high control mathematic mo
16、del 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 geometry domain. The main problem is the acquisition of the inverse model in the applications. Si
17、nce non-linear system is a complex system, and desired s t r i c t a n a l y t i c a l i n v e r s e i s v e r y 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
18、 complexity system, it becomes with the powerful e x p e c t a t i o n s t o o l t o s o l v e t h e p r o b l e m . 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 co
19、ntrol 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 i
20、s 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 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 e x p r e s s e d a s f o l l o w s : (1)
