基于模糊神经解耦控制的板型板厚控制系统仿真

面对板型板厚控制这一复杂、多变量耦合的非线性系统，本文提出了一种两级串联结构的模糊神经网络解耦控制策略，前级为自调整模糊控制器，后级为基于动态耦合特性的自适应神经网络解耦控制器，并从理论上证明了学习算法的收敛性。实现了无模型板型板厚综合控制。仿真结果表明，该控制系统收敛性好、抗干扰性强，取得令人满意的板型板厚控制精度。

Simulation of Strips Gauge and Flatness Control System Based on Fuzzy Neural Network Decoupling Control

Referring to plate flatness and gauge control system with multivariable nonlinear and strong coupling aswell as uncertain parameters, the paper proposes a two stage cascade fuzzy neural network decoupling control strategy, the former is a self - tuning fuzzy controller by using the intelligent weight function rulers, the latter is a self - adaptive neural network decoupling controller based on the learning algorithm of dynamic coupling .characteristic. It theoretically proves the convergence 0f learning algorithm. _Automatic flatness control （AFC） an,1 amomatic gauge control （AGC） are achieved. The simulation result shows that this control system has good convergence and disturbance resistance and gains a satisfied control accuracy of the flatness and the gauge.