基于RBF神经网络在带钢厚度控制中的应用

轧钢厚度控制系统的数学模型难以精确建立，传统的PID控制器的自适应能力较差，很难达到满意的控制效果。本文根据以上问题。提出了一种新的控制方法，即基于RBF神经网络自整定PID控制方法。这种控制方法结合了RBF神经网络和PID控制器的控制优势，不仅具有很强的自适应能力、鲁棒性。而且充分发挥了PID控制优势，并且将这种控制方法应用在带钢厚度的控制系统中，取得了很好的控制效果，证明了控制方案的正确性和有效性。

The application of neural network based on RBF in thickness control of strip steel

The system of rolling-thickness control is difficult to establish a accurate mathematical model,and the traditional PID controller has a poor adaptive ability,so the effect of control is always not satisfying. According to the problems above,This paper proposes a new control method,self-tuning PID controller based on RBF neural network. This control method integrates advantages of RBF neural network and the PID controller, not only has strong self-adapting ability and robustness,but also fully exerts the advantages of PID controller,and it achieved a very good control effect when used in strip thickness control system, that proved the correctness and effectiveness of this control method.