王莉, 葛平, 孙一康. 基于模糊RBF神经元网络的冷连轧板形板厚多变量控制[J]. 工程科学学报, 2002, 24(5): 556-559. DOI: 10.13374/j.issn1001-053x.2002.05.020
引用本文: 王莉, 葛平, 孙一康. 基于模糊RBF神经元网络的冷连轧板形板厚多变量控制[J]. 工程科学学报, 2002, 24(5): 556-559. DOI: 10.13374/j.issn1001-053x.2002.05.020
WANG Li, GE Ping, SUN Yikang. Strip Flatness and Gauge Multivariable Control at a Cold Tandem Mill Based on Fuzzy RBF Neural Network[J]. Chinese Journal of Engineering, 2002, 24(5): 556-559. DOI: 10.13374/j.issn1001-053x.2002.05.020
Citation: WANG Li, GE Ping, SUN Yikang. Strip Flatness and Gauge Multivariable Control at a Cold Tandem Mill Based on Fuzzy RBF Neural Network[J]. Chinese Journal of Engineering, 2002, 24(5): 556-559. DOI: 10.13374/j.issn1001-053x.2002.05.020

基于模糊RBF神经元网络的冷连轧板形板厚多变量控制

Strip Flatness and Gauge Multivariable Control at a Cold Tandem Mill Based on Fuzzy RBF Neural Network

  • 摘要: 针对板带材轧制是一个复杂的非线性过程,板形控制(AFC)和板厚控制(AGC)又是相互耦合的一个综合系统等特点,提出了一种基于模糊RBF神经元网络的冷连轧板形板厚多变量综合控制系统.仿真结果证明了此AFC-AGC控制系统具有良好的自适应跟随和抗扰性能,其控制效果优于传统的解耦PID控制.

     

    Abstract: Strip rolling is a very complicated nonlinear process, and flatness control (AFC) and gauge control (AGC) are a decoupled complex system. A kind of strip flatness and gauge complex control system is presented based on the fuzzy RBF neural network (FRBF) multivariable controller design method. Simulation results show that this kind of new controller has good performances of adaptively tracking target and resisting disturbances and is superior to the conventional decoupled PID control in terms of improving the strip flatness and gauge accuracy.

     

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