薄膜生产工艺收卷精度优化控制仿真

摘要

The difficulty of film coiling is to maintain the stability of the tension,and tension disturbance can lead to many problems,such as crease and interlayer sliding at the film surface,which can seriously affects the production quality.Tension control is a multivariable,strong coupling system which is influenced by factors such as roll speed,roll diameter,pressure and so on.The conventional PID controller has some limitations,the automatic adjustment of parameters and decoupling control among the multiple variables for the controller can not be realized easily with that.In view of the above difficulties,the influence factors of BOPP film production winding system was analyzed and tension control model was established based on MATLAB.Neural network,which has the characteristics including adaptivity,self-learning and nonlinearity,was used to optimize the control.Tthe results show that the parameters of the controller can be effectively adjusted in real time to keep stability of the tension,the decoupling control of tension and pressure is realized,and the precision of the winding system is effectively improved.The validity of the optimization strategy is verified.%薄膜卷取难点在于保持张力的稳定,张力的扰动会导致膜面折皱、层间滑动等问题,严重影响生产质量.张力调节受实时卷速、卷径、压力等因素的影响,是一个多变量、强耦合的系统.常规PID控制器具有一定局限性,难以实现控制器的参数自动调整及多变量间的解耦控制.针对上述难点,分析了BOPP薄膜生产线双工位收卷系统张力的各影响因素,建立了基于MATLAB的张力控制模型.利用神经网络自适应、自学习和非线性的特点对系统进行优化控制,仿真结果表明该控制算法能实时有效的调节控制器的参数以保持张力的稳定,且实现了收卷张力、压力间的解耦控制,提高了收卷系统的精度.验证了该优化策略的可行性.