基于非线性预测模型的单神经元自适应PID板形控制

为了解决传统PID板形控制精度低、速度慢、抗干扰能力差等问题,将BP神经网络和单神经元引入到板形的控制中,提出一种基于BP神经网络预测模型的单神经元自适应PID控制的板形控制策略。利用BP神经网络的非线性逼近能力和单神经元的自学习、自适应能力,通过两者的有机结合寻找一个最佳的P、I、D非线性组合控制律,实现对带钢板形缺陷的有效控制。仿真实验结果表明,该控制算法能很好地跟踪板形的目标设定值,提高了系统的控制精度,加快了系统的响应速度,并且具备较强的抗干扰能力。

Single Nerve Cell Adaptive PID Flatness Control Based on Nonlinear Prediction Model

The strategy of single-nerve-cell adaptive PID flatness control based on BP neural network prediction model is proposed in order to resolve the problem of low precision,slow speed and bad anti-interference ability in conventional PID flatness control.According to the combination of the nonlinear approach ability of BP neural network and the self-learning and adaptive ability of single nerve cell,an optimal nonlinear composite control rule of P,I and D is found to control flatness defects of strips effective...