基于神经网络和PID算法的数控机床并行混合控制模型

Parallel Hybrid Control Model for CNC Machine Based on Neural Network and PID Algorithm

  • 摘要: 针对数控机床低速运动时由于非线性摩擦造成的问题,提出了一种基于神经网络和PID算法的并行混合控制模型.当电机速度大于转换速度时使用PID控制,小于转换速度时使用神经网络控制器.神经网络为5个输入的单神经元,采用Hebb学习算法.分析表明,混合控制器使跟随误差的波动明显减小,机床运动变得平稳.利用可由用户编写伺服算法的多轴运动控制器(PMAC)进行了实验,验证了混合控制器的控制效果.

     

    Abstract: For the problem caused by nonlinear friction in CNC machine at low velocity,a parallel hybrid control model based on neural network and PID algorithm is proposed.While the motor speed is higher than the switching speed,the PID algorithm is adopted,otherwise,the neural network controller is used.The neural network is a single neuron with five inputs,and uses Hebb learning algorithm.Analysis shows that the hybrid controller greatly decreases the variation of following error,and the machine runs more smoothly.Experiments are made with the programmable multi-axis controller(PMAC) to validate the performance of the hybrid controller.

     

/

返回文章
返回