冷轧机厚度的模糊免疫PID控制
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摘要
厚度精度是衡量板带材轧机生产质量的一个重要标志。而高精度的板带材出口厚度需要通过厚度自动控制系统实现。为了提高成品板带材厚度精度,将模糊控制、生物免疫系统的反馈机理和传统PID控制相结合,构造了带钢厚度控制系统的模糊免疫PID控制器,利用免疫算法和模糊逻辑在线调整PID参数。仿真结果表明,基于模糊免疫PID控制器的轧机厚度控制系统在响应速度、超调量、稳定性和鲁棒性等方面均优于基于常规PID控制器的厚度控制系统,能够显著提高出口厚度精度。
Thickness precision is an important index of measuring the products quality of a plate and strip mill.The high accuracy of strip thickness is needed to be realized through the thickness automatic control(AGC) system.In order to improve the thickness accuracy of the finished strip,fuzzy control,feedback mechanism of biological immune system and traditional PID control are combined in this paper to construct a fuzzy immune PID controller for strip thickness control system,using immune algorithm and fuzzy logic to adjust PID parameters on-line.Simulation results show that the fuzzy immune PID controller were better than thickness controller system based on conventional PID controller in response speed,overshoot,stability and robustness.It can improve the precision of the export thickness remarkably.
Thickness precision is an important index of measuring the products quality of a plate and strip mill.The high accuracy of strip thickness is needed to be realized through the thickness automatic control(AGC) system.In order to improve the thickness accuracy of the finished strip,fuzzy control,feedback mechanism of biological immune system and traditional PID control are combined in this paper to construct a fuzzy immune PID controller for strip thickness control system,using immune algorithm and fuzzy logic to adjust PID parameters on-line.Simulation results show that the fuzzy immune PID controller were better than thickness controller system based on conventional PID controller in response speed,overshoot,stability and robustness.It can improve the precision of the export thickness remarkably.
引文
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[9]王宏文,吴玲玲,唐永学,等.液压位置伺服系统的模糊免疫自适应PID控制[J].机床与液压,2011,39(12):38-41.
[10]邓婧,李兴源,魏巍.基于模糊免疫算法的汽轮发电机组OPC控制[J].电力系统及其自动化学报,2011,23(3):1-7.
[11]GHERBI S,BOUCHAREB F.Optimal Tuning of a Fuzzy Immune PID Parameters to Control a Delayed System[J].International Journal of Electrical,Computer,Electronics and Communication Engineering,2014,8(6):853-857.
[12]YUAN G L,LIU J Z.The design for feed water system of boiler based on fuzzy immune smith control[J].Journal of Computers,2012,7(1):278-283.
[13]HAMEED W I,MOHAMAD K A.Strip thickness control of cold rolling mill with roll eccentricity compensation by using fuzzy neural network[J].Engineering,2014(6):27-33.
[2]SALA A,GUERRA T,BABUSKA R.Perspectives of fuzzy systems and control[J].Fuzzy Sets and Systems,2005,156(3):432-444.
[3]NHIVEKAR G S,NIRMALE S S,MUDHOLKER R R.Implementation of fuzzy logic control algorithm in embedded microcomputers for dedicated application[J].International Journal of Engineering,Science and Technology,2011,3(4):276-283.
[4]段慧达,刘德君,周振雄,等.模糊自适应PID反馈补偿控制在轧钢控制系统中的应用[J].电气传动,2007,37(4):41-43.
[5]PENG Y,LUO X,WEI W.A new control method based on artificial immune adaptive strategy[J].Elektronika ir Elektrotechnika,2013,19(4):3-8.
[6]OMKAR S N,KHANDELWAL R,YATHINDRA S,et al.Artificial immune system for multi-objective design optimization of composite structures[J].Engineering Applications of Artificial Intelligence,2008,21(8):1416-1429.
[7]REN X,DU F,HUANG H,et al.Application of improved fuzzy immune PID controller to bending control system[J].Journal of Iron and Steel Research,International,2011,18(3):28-33.
[8]谢仕宏,高筱筱,李林涛,等.化工过程p H值的模糊免疫PID控制[J].计算机测量与控制,2014,22(1):76-78.
[9]王宏文,吴玲玲,唐永学,等.液压位置伺服系统的模糊免疫自适应PID控制[J].机床与液压,2011,39(12):38-41.
[10]邓婧,李兴源,魏巍.基于模糊免疫算法的汽轮发电机组OPC控制[J].电力系统及其自动化学报,2011,23(3):1-7.
[11]GHERBI S,BOUCHAREB F.Optimal Tuning of a Fuzzy Immune PID Parameters to Control a Delayed System[J].International Journal of Electrical,Computer,Electronics and Communication Engineering,2014,8(6):853-857.
[12]YUAN G L,LIU J Z.The design for feed water system of boiler based on fuzzy immune smith control[J].Journal of Computers,2012,7(1):278-283.
[13]HAMEED W I,MOHAMAD K A.Strip thickness control of cold rolling mill with roll eccentricity compensation by using fuzzy neural network[J].Engineering,2014(6):27-33.