基于RBF神经网络整定的经纱张力PID控制系统

针对目前国内大多织机经纱张力控制系统采用传统PID控制,对数学模型依赖度高,难于达到较好控制效果的缺陷,提出了一种基于Kalman滤波器的RBF径向神经网络整定的PID控制算法。这种控制算法采用3输入、单输出的RBF径向神经网络对系统性能学习以寻找出最佳的PID组合,Kalman滤波器有效地滤掉了织机中的各种噪声,实现经纱张力值的恒定。仿真实验结果表明,基于神经网络整定的经纱张力控制系统的控制效果和动态性能都明显优于传统PID控制。

Adaptive PID control for warp tension system based on RBF neural network

At present,many of the looms uses conventional PID control in China,which relays on the mathematical model and can′t get a good control result.Because of conventional PID control′s defects,an adaptive PID control algorithm based on RBF neural network with a Kalman filter is designed.The control algorithm uses three input-single output RBF radial neural network which learns system′s performance to find the best combination of PID,uses a Kalman filter which effectively filters out various noises of the loom,and achieves a constant tension value.The simulate results indicate that control effect and dynamic performance for adaptive PID control for warp tension system based on neural network are obviously superior to those of conventional PID control.