热连轧活套系统解耦控制（英文）
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摘要
热连轧机中的活套系统是多变量强耦合、非线性、多约束的复杂对象,其解耦控制问题一直是控制界关注的热点。采用PID神经元网络解耦控制方法来消除活套高度和轧件张力之间的耦合,PID神经元网络连接权值由粒子群算法进行学习优化。仿真结果表明所建模型和所提出控制方法的有效性。新的方法可在大范围内克服系统的非线性和强耦合问题,具有较强的鲁棒性。
Looper system of hot strip mills is complex due to characteristics of multi-variability,strong coupling,nonlinearity and multi-constrain. Therefore,the decoupling control of looper system has received increasing attention in the field of control. In this study,the decoupling control technology based on PID neural network( PIDNN) was used to eliminate the coupling between the looper height and strip tension,and the particle swarm optimization algorithm was adopted to optimize weights of neural networks. The simulation results show that the proposed control method in this study were effective for the decoupling control. The established method could overcome the problem of nonlinear and strong coupling in wide range,and it exhibited strong robustness.
Looper system of hot strip mills is complex due to characteristics of multi-variability,strong coupling,nonlinearity and multi-constrain. Therefore,the decoupling control of looper system has received increasing attention in the field of control. In this study,the decoupling control technology based on PID neural network( PIDNN) was used to eliminate the coupling between the looper height and strip tension,and the particle swarm optimization algorithm was adopted to optimize weights of neural networks. The simulation results show that the proposed control method in this study were effective for the decoupling control. The established method could overcome the problem of nonlinear and strong coupling in wide range,and it exhibited strong robustness.
引文
[1]ZHOU Jianxin.Adaptive decoupling control for looper system of hot strip mill based on chaos PSONN-PID neural network[J].Hydromechatronics Engineering,2015,44(6):30-35.
[2]SHU Huailin,SHU Hua.PID neural network temperature control system in plastic injecting moulding machine[C]//Third International Conference on Natural Computation,2007:39-43.
[3]WANG Jiesheng,ZHANG Yong.PID-ANN decoupling controller of ball mill pulverizing system based on particle swarm optimization method[C]//Chinese Control and Decision Conference,2008:1424-1428.
[4]MENG Lei,ZOU Yun,WANG Zhizhen,et al.Design of an improved PID neural network controller based on particle swarm optimization[C]//China Automation Congress,2015:151-154.
[5]GUO Aiwen,LI Jinping,YANG Jiebin.PIDNN decoupling control of doubly fed hydro-generator system based on PSOmethod[C]//Proceeding of 30th Chinese Control Conference,2011:2698-2701.
[6]SHU Huailin.PID neural network and its control system[M].Beijing:National Defense Industry Press,2006.
[7]YANG Huafeng,LI Yong,TIAN Anhong,et al.Particle swarm optimization based on adaptive mutation and diminishing inerita weights[C]//Proceeding of 9th International Conference on Natural Computation,2013:549-553.
[8]XIE Xiaofeng,ZHANG Wenjun,YANG Zhilian.Overview of particle swarm optimization[J].Control and Decision,2003,18(2):129-134.
[9]WANG Xiaochuan,SHI Feng,YU Lei,et al.Matlab neural network 43 cases analysis[M].Beijing:Beijing University of Aeronautics and Astronautics Press,2013.
[2]SHU Huailin,SHU Hua.PID neural network temperature control system in plastic injecting moulding machine[C]//Third International Conference on Natural Computation,2007:39-43.
[3]WANG Jiesheng,ZHANG Yong.PID-ANN decoupling controller of ball mill pulverizing system based on particle swarm optimization method[C]//Chinese Control and Decision Conference,2008:1424-1428.
[4]MENG Lei,ZOU Yun,WANG Zhizhen,et al.Design of an improved PID neural network controller based on particle swarm optimization[C]//China Automation Congress,2015:151-154.
[5]GUO Aiwen,LI Jinping,YANG Jiebin.PIDNN decoupling control of doubly fed hydro-generator system based on PSOmethod[C]//Proceeding of 30th Chinese Control Conference,2011:2698-2701.
[6]SHU Huailin.PID neural network and its control system[M].Beijing:National Defense Industry Press,2006.
[7]YANG Huafeng,LI Yong,TIAN Anhong,et al.Particle swarm optimization based on adaptive mutation and diminishing inerita weights[C]//Proceeding of 9th International Conference on Natural Computation,2013:549-553.
[8]XIE Xiaofeng,ZHANG Wenjun,YANG Zhilian.Overview of particle swarm optimization[J].Control and Decision,2003,18(2):129-134.
[9]WANG Xiaochuan,SHI Feng,YU Lei,et al.Matlab neural network 43 cases analysis[M].Beijing:Beijing University of Aeronautics and Astronautics Press,2013.