Application in composite machine using RBF neural network based on PID control
نویسنده
چکیده
In the absence of solvent composite machine, because the radius of drum winding and rewinding roller in the transmission process is changing. With the coiled material rolls diameter more and more large, and put the curly size getting smaller and smaller, this has the certain difficulty for the tension control. Therefore, good tension control is non solvent composite is very important. Analyzed the reason and the tension control mathematical model generation composite machine tension in the BOPP production line, for the constant tension control of composite 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.
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