基于RBF神经网络控制的带式输送机功率平衡研究

Research on power balance of belt conveyor based on RBF neural network control

针对采用传统PID控制方案进行带式输送机功率平衡控制存在的控制精度低、延时较长的问题,研究基于RBF神经网络控制方案。利用RBF神经网络的自适应、自学功能,对多电动机驱动的带式输送机驱动装置的油膜油压进行精确控制,从而调节电动机的输出功率,使其与负载大小匹配。在详细分析RBF神经网络控制原理的基础上,给出功率平衡控制方案以及软件实现流程。经Simulink仿真结果证明,所提方案能够实现多电动机带式输送机功率平衡控制,能够适应其时变、大滞后系统特性。

In view of the problems of low control precision and long delay in the power balance control of belt conveyor with traditional PID control scheme,the RBF neural network control scheme is studied.The oil film oil pressure of belt conveyor driven by multi motor is precisely controlled by using the self-adaptive and self-learning function of RBF neural network,so as to adjust the output power of the motor to match the load.Based on the detailed analysis of the control principle of RBF neural network,the power balance control scheme and the software implementation flow are given.The simulation results of Simulink show that the proposed scheme can realize the power balance control of multi motor belt conveyor and adapt to the characteristics of time-varying and large lag system.