带式输送机启动控制策略的研究

发布时间：2018-02-03 00:22

本文关键词： 带式输送机有限元分析法 S型曲线模糊理论 RBF神经网络　出处：《合肥工业大学》2013年硕士论文　论文类型：学位论文

【摘要】：带式输送机已经被煤炭、化工、冶金、矿山、港口、电力等各行各业广泛应用，随着带式输送机逐渐向长距离、重负荷、高速度方向发展，其启动问题日益突出。带式输送机系统，在其启动过程中会产生变形，如果采取直接启动方式或者启动时间过短，其加速度就会很大，使得输送带的粘弹性变形很大，最终导致动张力很大，，直接对输送机系统各元部件产生冲击，缩短它们的使用寿命。此外，带式输送机工作环境也比较恶劣，因此对控制器的鲁棒性也有一定的要求。因此，大型带式输送机的启动过程是一个很重要的研究课题。针对上述问题，本文做了如下几方面的工作：首先，采用有限元分析方法建立起带式输送机系统的动力学模型。该模型着重考虑了输送带的粘弹特性，通过对几种典型的粘弹性体模型的动力学特征的比较分析，最终确定Vogit模型作为输送带微元段的动力学模型，对众微元段的动力学模型进行有顺序的排列组合，最终形成了整个输送机系统的简易动力学模型。其次，启动过程采用S型速度可控曲线来控制带式输送机的加速度。带式输送机的启动本身就是一个不稳定的过程，S型速度启动曲线能够实现系统的平稳启动，有效地降低震荡。然后，重点介绍PID控制，模糊理论，人工神经元网络等基础理论，并在MATLAB的基础上，对带式输送机的启动过程作了仿真实验，得到一系列的仿真效果图，为后面的工作奠定了理论基础。最后，针对上述控制器的不足，学习研究了RBF神经网络。一方面，利用RBF神经网络进行系统辨识，优化了学习规则，对原有的单神经元控制器进行了改进；另一方面，将模糊RBF神经网络作为该带式输送机系统的控制器。仿真结果表明，基于RBF神经网络的控制器都得到了相当不错的控制效果。
[Abstract]:Belt conveyor has been widely used in coal, chemical, metallurgical, mining, port, power and other industries, with the belt conveyor gradually to the direction of long distance, heavy load and high speed. Belt conveyor system will produce deformation in its starting process. If it adopts the direct starting mode or the start time is too short, its acceleration will be very large. The viscoelastic deformation of the conveyor belt is very large, resulting in a large dynamic tension, directly impact on the components of the conveyor system, shorten their service life. In addition, the belt conveyor working environment is also relatively bad. Therefore, the robustness of the controller is also required. Therefore, the start-up process of large belt conveyor is an important research topic. In view of the above problems, this paper has done the following work: Firstly, the dynamic model of belt conveyor system is established by finite element method, which focuses on the viscoelastic characteristics of belt. Through the comparative analysis of the dynamic characteristics of several typical viscoelastic models, the Vogit model is finally determined as the dynamic model of the conveyor belt microelement segment. A simple dynamic model of the whole conveyer system was formed by arranging and combining the dynamic model of the multitudinous microelement section in sequence. Secondly, the acceleration of belt conveyor is controlled by S-type speed control curve. The start of belt conveyor itself is an unstable process. The S-type speed start curve can realize the steady start of the system and reduce the oscillation effectively. Then, the basic theories such as PID control, fuzzy theory, artificial neural network and so on are introduced, and on the basis of MATLAB, the start-up process of belt conveyor is simulated. A series of simulation effect diagrams are obtained, which lays a theoretical foundation for the later work. Finally, aiming at the deficiency of the controller mentioned above, the RBF neural network is studied. On the one hand, the RBF neural network is used to identify the system and the learning rules are optimized. The original single neuron controller is improved. On the other hand, the fuzzy RBF neural network is used as the controller of the belt conveyor system. The simulation results show that the controller based on the RBF neural network has quite good control effect.
【学位授予单位】：合肥工业大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TH222;TP273

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