Self-learning PID Control for X-Y NC Position Table with Uncertainty Base on Neural Network
Abstract: An adaptive radical
basis function (RBF) neural network PID control scheme for X-Y position table is
proposed by the paper. Firstly, X-Y position table model is established,
controller based on neutral network is used to learn adaptive and compensate
uncertainty model of X-Y position table, neutral network is used to study
model. PID neural network controller base on augmented variable method is
designed. PID controller is used as assistant direction error controller,
neural network parameters base on stochastic gradient algorithm can be adjust
adaptive on line. The simulation results show that the presented controller has
important engineering value.
Author: Hu Xiaoping, Wang
Chao, Zhang Wenhui, Ma Jing
Journal Code: jptkomputergg140072
