【作者】 李莉；

【导师】 刘惠康；

【作者基本信息】 武汉科技大学 ， 控制理论与控制工程， 2008， 硕士

【摘要】 智能控制技术在解决现代复杂被控对象的控制上比传统控制技术有明显的优越性,而基于神经网络的控制由于其所特有的并行处理、自适应、自组织和自学习能力,在自动控制领域中有广泛的应用前景,其中常规PID控制具有算法简单,可以改善系统的动态特性和稳态特性的优点,因而被广泛的应用于拉力控制系统中。但是在拉丝机进行卷取拉力控制时,由于卷取中卷径的变化以及其它的一些不可检测的因素的影响,相应的带来了系统参数的变化,传统PID控制器的参数往往是针对一种情况进行整定,因此很难保持拉力的恒定。针对这种情况,本文设计出一种模糊神经网络自适应PID控制器,根据卷取拉力控制中的实时变化,利用模糊神经网络控制器对PID控制器的参数进行在线自调整,使其能够适应被控制过程中对象的变化,从而实现恒拉力卷取控制。本文较为详细地阐述了模糊神经网络控制原理。模糊神经网络控制系统主要由模糊控制系统和神经网络控制系统两者结合起来,取长补短,将神经网络的学习能力引用到模糊控制系统中去,充分利用其自组织、自学习能力,实现模糊规则的自寻优及隶属函数的自调整,从而克服神经网络结构难以确定以及模糊控制无自学习能力的缺点,能适应一些对控制要求较高的系统。该系统在实验室仿真成功,系统工作稳定,操作方便,能够获得满意的性能指标,具有较好的快速跟随性,且稳态精度高,调节时间有了明显改善,提高了系统的抗干扰能力,全面的改善了系统的动态性能。经过一段时间的实际应用,效果良好,明显取得了比常规PID控制器在拉力控制中更好的控制特性。更多还原

【Abstract】 The technology of intelligent control has more advantage than tradition technology in controlling complex objects of modern industrial control. And the intelligent control as an effective way has great value in the auto control application potentially due to its ability of parallel processing, adapting, self-organizing and self-study. And because the algorithm of the conventional PID controller is simple and can improve the systematic dynamic characteristic and the steady characteristic, it is widely applied to tension control system. But while carrying on the roll tension control, the systematic parameter is changed according to the change of the diameter of the roller and the influence of some other detectable factors. The parameter of conventional PID controller is often exactly designed to a kind of situation and is invariable. So the conventional PID controller is very difficult to keep tension well. Then we design a kind of fuzzy neural network adaptive PID controller. In view of the real change of roll tension control, we use the fuzzy neural network controller to change the parameter of PID controller in time in order to enable it to adapt for the process with the change of object. Then we can realize the permanent tension control.In this article,we elaborate the fuzzy neural network control principle in detail. The fuzzy neural network control system mainly includes two parts: the fuzzy control system and the neural network system. We can adapt the learning capability of the neural network to the fuzzy control system and use it by the organization and the learning capability to realize seeking superior function of the fuzzy rule and adjust the subordination. The fuzzy neural network control system can overcome difficulty of the define of the neural network structure as well as the shortcoming of the learning capability of the fuzzy control. It can apply to more complicated system.We simulate this system in laboratory successfully. The system work stably and operate easily. It can obtain the satisfied performance and high stability, follows the source well. The time of control improve obviously. It improves the system anti jamming ability and comprehensibly improves the dynamic performance of system. After a period of practical application, it has obviously obtained the better control characteristic of tension control compared to the conventional PID controller.更多还原