液压弯辊系统具有时变、非线性、外界干扰严重等特点,难以建立精确的用于控制的数学模型。针对该问题,在预先设计了一个PID控制器的基础上,用模糊神经网络设计了一种具有自适应能力的控制器。仿真实验表明,同常规PID控制器相比,该控制器可以克服弯辊系统的参数改变对控制性能的影响,极大地提高了系统的抗干扰能力,降低了控制系统性能对弯辊系统解析模型精度的依赖程度。结果证明该控制方法对于弯辊系统的控制是有效的。
In view of the time variability and the nonlinear character in rolling mills, for example UC rolling mill, a AFNNC with selfadapting ability is developed after a PID controller have been finished. Compared with PID controller, the simulation shows that AFNNC has more desirable performance in selfadapting ability and robustness. This control method is very useful for improving the quality of shape control.
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