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Abstract. In the BOPP （Biaxially Oriented Polypropylene） production line， the tension size and smooth film received change volume has a decisive effect on the rolling quality， casting machine is a complicated electromechanical control system， tension control of casting machine are the main factors that influence the production quality. Analyzed the reason and the tension control mathematical model generation casting machine tension in the BOPP production line， for the constant tension control of casting machine， put forward a kind of improved PID control method based on RBF neural network. By the method of Jacobian information identification of RBF neural network， combined with the incremental PID algorithm to realize the self-tuning tension control parameters， control simulation and implementation of the model using Matlab software programming. The simulation results show that， the improved algorithm has better control effect than the general PID.
Keywords： Control PID algorithm， Jacobian information identification， RBF neural network， Matlab
0 Introduction
In recent years， the development of China packaging industry develop rapidly， in which the film high speed， smooth coiling in multi-layer coextrusion drooling film production is especially important. According to these characteristics， this article in order to maintain constant tension as the core， dynamic relationship between the size and speed of tension and linear speed， Study on the variation regularity of receive roll when changing winding motor， by controlling the motor speed difference of indirect tension control， at the same time using RBF neural network PID controller regulates the collection of direct compensation of tension roll speed， so as to realize the high-speed collecting film in roll when changing the constant tension control. Simulation and experiments show that， the presented control method has good control effect.
Tension control system of cast film processing equipment machine usually by the unwinding and rewinding， and a series of interstand tension roller， etc.. In order to ensure the wrinkles and cutting the material phenomenon does not occur and to run at a certain speed ， the constant tension control play a decisive role. Tension exists mainly in the parent roll unwinding and finished products in the process of winding roll. Tension arises mainly from between the film Friction and the reel and the effect of driving force. This paper focuses on the analysis of casting machine control tension， as shown in Figure 1 for the winding tension process analysis. Design radial basis transfer function for the hidden layer of：
Each neuron in the hidden layer weights and the thresholds of the center position vector of radial basis function and based wide vector corresponding to the specified. Every linear output neurons by weighting these radial basis function and composition. As long as each layer has the correct weights and thresholds， and have enough neurons in hidden layer， function as radial basis function networks to any arbitrary accuracy to approximate arbitrary. The completion of the training of the network in the learning process of neural network set up， in order to achieve the goal of error， the sample point parameter approximation function reaches the target parameter requirements.
3.2 The simulation results of tension control
According to the above are derived by the RBF neural network controller and model identification learning algorithm， using Matlab simulation. The sampling period . The RBF tuning of PID after the output response as shown in Figure 3， figure for cutting machine at the moment the actual output tension value， and the tension of the given as desired value.
As you can see from Figure 3， the RBF identification system identification RBF neural network controller has fast response speed， can play fast tracking effect， model identification online.
This paper did simulation results were obtained in the absence of any field interference situations， therefore， the stability of the system and this is a scene with no comparable. After the PID tuning output tension curve， the output curve is better in overshoot， adjust time characteristics still than the actual site setup PID more.
As shown in Figure 4 for the PID parameter tuning curves， these curves reflect， and to realize the online identification， stabilized quickly and reaches the setting optimal results. Through online parameter controlling system， to meet the actual output value of static tension control index and the input value， and has good dynamic performance.
4 Conclusion
Because the traditional PID control algorithm in the proportion， integral， differential coefficient in the design stage set after week constant， do not have the ability to learn， self adaptive. Therefore， the robustness of using traditional PID tension controller of poor control performance needs to be improved. Simulation results show that PID tension control based on RBF neural network， through online identification process model is established and provide gradient information for the neuron controller， realizes the online identification and online control purposes； and high control accuracy， good dynamic characteristic， has self adaptability and robustness.
References
[1] Li Wenyu. Slitter rewinding tension control analysis of [J]. Plastic packaging of.2002，12 （3）： 14-15.
[2] Haykin S. neural network based on the [M].2 version. Ye Shiwei， Shi Zhongzhi， trans. Beijing： Mechanical Industry Press，2004：168 169
[3] Shi Zhongzhao. Neural network control theory [M]. Xi'an： Northwestern Polytechnical University press， 1999：85-90.
[4] Wang Jiangjiang， Zhang Chunfa， Jing Youyin. Self-adap tive RBF neural network PID control in exhaust temperature of micro-turbine[C] Proceedings of the Seventh International Confere nce on Machine Learning and Cybernetics， 2008：2131-2136.
[5] Elanayar V T S， Shin Y C. Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems [J].IEEE Transactions on Neural Network，1994，5 （4）：594-603.
Keywords： Control PID algorithm， Jacobian information identification， RBF neural network， Matlab
0 Introduction
In recent years， the development of China packaging industry develop rapidly， in which the film high speed， smooth coiling in multi-layer coextrusion drooling film production is especially important. According to these characteristics， this article in order to maintain constant tension as the core， dynamic relationship between the size and speed of tension and linear speed， Study on the variation regularity of receive roll when changing winding motor， by controlling the motor speed difference of indirect tension control， at the same time using RBF neural network PID controller regulates the collection of direct compensation of tension roll speed， so as to realize the high-speed collecting film in roll when changing the constant tension control. Simulation and experiments show that， the presented control method has good control effect.
Tension control system of cast film processing equipment machine usually by the unwinding and rewinding， and a series of interstand tension roller， etc.. In order to ensure the wrinkles and cutting the material phenomenon does not occur and to run at a certain speed ， the constant tension control play a decisive role. Tension exists mainly in the parent roll unwinding and finished products in the process of winding roll. Tension arises mainly from between the film Friction and the reel and the effect of driving force. This paper focuses on the analysis of casting machine control tension， as shown in Figure 1 for the winding tension process analysis. Design radial basis transfer function for the hidden layer of：
Each neuron in the hidden layer weights and the thresholds of the center position vector of radial basis function and based wide vector corresponding to the specified. Every linear output neurons by weighting these radial basis function and composition. As long as each layer has the correct weights and thresholds， and have enough neurons in hidden layer， function as radial basis function networks to any arbitrary accuracy to approximate arbitrary. The completion of the training of the network in the learning process of neural network set up， in order to achieve the goal of error， the sample point parameter approximation function reaches the target parameter requirements.
3.2 The simulation results of tension control
According to the above are derived by the RBF neural network controller and model identification learning algorithm， using Matlab simulation. The sampling period . The RBF tuning of PID after the output response as shown in Figure 3， figure for cutting machine at the moment the actual output tension value， and the tension of the given as desired value.
As you can see from Figure 3， the RBF identification system identification RBF neural network controller has fast response speed， can play fast tracking effect， model identification online.
This paper did simulation results were obtained in the absence of any field interference situations， therefore， the stability of the system and this is a scene with no comparable. After the PID tuning output tension curve， the output curve is better in overshoot， adjust time characteristics still than the actual site setup PID more.
As shown in Figure 4 for the PID parameter tuning curves， these curves reflect， and to realize the online identification， stabilized quickly and reaches the setting optimal results. Through online parameter controlling system， to meet the actual output value of static tension control index and the input value， and has good dynamic performance.
4 Conclusion
Because the traditional PID control algorithm in the proportion， integral， differential coefficient in the design stage set after week constant， do not have the ability to learn， self adaptive. Therefore， the robustness of using traditional PID tension controller of poor control performance needs to be improved. Simulation results show that PID tension control based on RBF neural network， through online identification process model is established and provide gradient information for the neuron controller， realizes the online identification and online control purposes； and high control accuracy， good dynamic characteristic， has self adaptability and robustness.
References
[1] Li Wenyu. Slitter rewinding tension control analysis of [J]. Plastic packaging of.2002，12 （3）： 14-15.
[2] Haykin S. neural network based on the [M].2 version. Ye Shiwei， Shi Zhongzhi， trans. Beijing： Mechanical Industry Press，2004：168 169
[3] Shi Zhongzhao. Neural network control theory [M]. Xi'an： Northwestern Polytechnical University press， 1999：85-90.
[4] Wang Jiangjiang， Zhang Chunfa， Jing Youyin. Self-adap tive RBF neural network PID control in exhaust temperature of micro-turbine[C] Proceedings of the Seventh International Confere nce on Machine Learning and Cybernetics， 2008：2131-2136.
[5] Elanayar V T S， Shin Y C. Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems [J].IEEE Transactions on Neural Network，1994，5 （4）：594-603.