低松驰预应力钢丝稳定化处理生产线系统控制的研究
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摘要
低松弛预应力钢丝是一种很有前途的高强度建筑用钢材，本文根据低松弛预应力钢丝稳定化处理生产线的工艺要求，对其电气系统进行了全面、细致的分析，对重要的工艺参数—回火温度的控制进行了详细讨论。主要内容如下：
一、首先根据低松弛预应力钢丝稳定化处理生产线的工艺要求，设计了控制系统的硬件、软件，并且就系统的抗干扰措施进行了讨论。
二、感应加热的过程是一个很复杂的工业过程，它涉及电、磁及热等多种物理过程。文章对系统加热过程的原理进行了分析，然后在保证一定精度的前提下，建立了对象工作点附近的近似模型，采用数值计算的方法模拟了系统的动态特性。
三、利用工业界常用的前馈—串级控制方案，对钢丝回火温度进行控制，取得了较好效果。
四、简介了普通模糊控制器的设计，讨论了一些模糊控制器的改进方案，然后引入了一种参数自调整Fuzzy—PI控制器。仿真结果表明，其控制性能要优于常规的FI控制。
五、CMAC神经网络可以充分逼近任意复杂的非线性关系，可学习和适应不知道或不确定的系统。CMAC神经网络逆模学习控制策略具有鲁棒性强、适应性良好，权值训练时间短等优点。十分适用于参数未知、时变的工业控制工程。
Low-relaxation prestressed steel wire is one kind of high degree of strength steels, and it is well used in construction industry. This paper based on the product technics, analysis the electric control system in detail. This thesis focuses on the control of the temper temperature, which is the core technics parameter. This paper contains following parts:
The first section: Based on the product technics ,designed the hardware structure and the software. The anti-jamming technique also be discussed.
The second section: The process of induce heating is a complex process. After analysis the heating mechanism, this thesis build up an approximate model for the system.
The third section: Used the feedforward--cascade control strategy to control the temper temperature, and acquires a good result.
The fourth section: Introduced design of the normal fuzzy controller, and produced some improved schemes. After this, this thesis proposed a fuzzy-Pi controller what uses a self-regulating scaling method. The simulation result illustrates that the fuzzy-Pi control is better than the normal PI controller.
The five section: The CMAC neural network will approach any complicated nonlinear relationship sufficiently. The network could study some unknown systems. The inverse model learning control strategy of CMAC neural network owned good quality in robustness, adaptability and short rights training time. It is adapt to the industry process which parameter is uncertain or unknown.
一、首先根据低松弛预应力钢丝稳定化处理生产线的工艺要求，设计了控制系统的硬件、软件，并且就系统的抗干扰措施进行了讨论。
二、感应加热的过程是一个很复杂的工业过程，它涉及电、磁及热等多种物理过程。文章对系统加热过程的原理进行了分析，然后在保证一定精度的前提下，建立了对象工作点附近的近似模型，采用数值计算的方法模拟了系统的动态特性。
三、利用工业界常用的前馈—串级控制方案，对钢丝回火温度进行控制，取得了较好效果。
四、简介了普通模糊控制器的设计，讨论了一些模糊控制器的改进方案，然后引入了一种参数自调整Fuzzy—PI控制器。仿真结果表明，其控制性能要优于常规的FI控制。
五、CMAC神经网络可以充分逼近任意复杂的非线性关系，可学习和适应不知道或不确定的系统。CMAC神经网络逆模学习控制策略具有鲁棒性强、适应性良好，权值训练时间短等优点。十分适用于参数未知、时变的工业控制工程。
Low-relaxation prestressed steel wire is one kind of high degree of strength steels, and it is well used in construction industry. This paper based on the product technics, analysis the electric control system in detail. This thesis focuses on the control of the temper temperature, which is the core technics parameter. This paper contains following parts:
The first section: Based on the product technics ,designed the hardware structure and the software. The anti-jamming technique also be discussed.
The second section: The process of induce heating is a complex process. After analysis the heating mechanism, this thesis build up an approximate model for the system.
The third section: Used the feedforward--cascade control strategy to control the temper temperature, and acquires a good result.
The fourth section: Introduced design of the normal fuzzy controller, and produced some improved schemes. After this, this thesis proposed a fuzzy-Pi controller what uses a self-regulating scaling method. The simulation result illustrates that the fuzzy-Pi control is better than the normal PI controller.
The five section: The CMAC neural network will approach any complicated nonlinear relationship sufficiently. The network could study some unknown systems. The inverse model learning control strategy of CMAC neural network owned good quality in robustness, adaptability and short rights training time. It is adapt to the industry process which parameter is uncertain or unknown.
引文
[1] 胡晓东．我国低松弛预应力钢丝及钢绞线的发展和展望．钢铁厂设计，1996，6：29～33
[2] 张秀凤．我国预应力钢材的现状及发展趋势(续完)．金属制品，2002，28(4)：1～3
[3] 俞勇祥，陈辉明等．感应加热电源的发展．金属热处理，2000(8)：28～29
[4] 林渭勋等．可控硅中频电源．北京：机械工业出版社，1983
[5] 潘天明．工频和中频感应炉，北京：冶金工业出版社，1983
[6] 潘天明．现代感应加热装置．北京：冶金工业出版社，1996
[7] 邵惠鹤．工业过程高级控制，上海交通大学出版社，1996
[8] 金以慧．过程控制，北京：清华大学出版社，1993
[9] 李阳春．自动控制理论在火电厂热工自动化中的应用．[学位论文]．杭州：浙江大学博士论文，2001
[10] Astrom K.J. Toward intelligent control. IEEE Control Systems Magazine , Volume: 9 Issue: 3 , April 1989 : 60～64
[11] 李士勇．模糊控制·神经控制和智能控制论，哈尔滨工业大学出版社，1996
[12] 祁珊，王世栋等．稳定化处理对高碳预应力钢丝性能的影响．热加工工艺，1998(1)：24—25
[13] 王幸之等．单片机应用系统抗干扰技术．北京：北京航空航天大学出版社，2000
[14] 金天均，邵鸣．微机化晶闸管中频电源启动方案设计．电力电子技术，1994，28(1)：14～17
[15] Roffel B., Rijinsdorp J. E.. Process Dynamics. Cntrol and Protection. ANN Arbor Science, 1982
[16] 王骥程．过程动态模型．浙江大学出版社，1994
[17] 丁萃箐．换热设备动态特性计算．北京：水利电力出版社，1993
[18] Ray R, Bowman H.F. A Nonlinear Dynamic Model of a Once-through Subcritical Steam Generator. Trans of the ASME, 1976(9)
[19] Astrom K.J. Autonomous process control. Control Applications, Second IEEE Conference on , vol. 2, 573～580, 1993
[20] 王骥成，祝和云．化工过程控制工程．北京：化学工业出版社，1991
[21] Hang C.C. , Astrom K. J. , Ho W.K. Refinements of the Ziegler-Nichols
tuning formula Control Theory and Applications.. IEE Proceedings D, Vol.138 Issue: 2 , 111～118, March 1991
[22] Ziegler J. G, Nichols N. B. Optimum settings for automatic controllers. Transaction of ASME, 1942, 64:759～768
[23] 王伟，张晶涛，柴天佑，PID参数先进整定方法综述．自动化学报，2000，26(3)：347～355
[24] 陈元杰，汪洋，苏宏业等．串级控制系统的PID参数自动整定算法．控制与决策，1996，11(5)：580～583
[25] Panagopoulos H., Astrom K.J., Hagglund T. A numerical method for design of PI controllers. Control Applications, Proceedings of the 1997 IEEE International Conference on , 1997, 417～422
[26] 薛定宇．反馈控制系统设计与应用—MATLAB语言与应用．清华大学出版社，2000
[27] 诸静．模糊控制原理与应用．北京：机械工业出版社．1996
[28] Kevin M. Passino, Stephen Yurkovich. Fuzzy Control.清华大学出版社，2001
[29] 张乃尧，阎平凡．神经网络与模糊控制．清华大学出版社，1998
[30] Lee C.C. Fuzzy logic in control systems: fuzzy logic controller. Part Ⅰ Ⅱ. Systems, Man and Cybernetics, IEEE Transactions on, Volume: 20 Issue: 2 , 404～435, Mar/Apr 1990
[31] 陶永华．新型PID控制及其应用．北京：机械工业出版社，2000
[32] Liang Xiaoyin , Qu Beimin. Fuzzy-PID controller. TENCON '93. Proceedings, Computer, Communication, Control and Power Engineering, 1993 IEEE Region 10 Conference on Issue: 0,1993 , vol. 4, 296～299
[33] 胡家耀，吴植翘，宋寿山．参数自调整Fuzzy—PI调节器．信息与控制，1987(6)：26～33
[34] Dunlop J. A., Burnham K. J., James D. J.G., King, P.J. Aself-regulating scaling method for fuzzy control .Control Applications, 1994, Proceedings of the Third IEEE Conference on, 24-26 Aug 1994, vol. 1, 683～687
[35] Sobol W., Korwin M. J.,Goraczko M.,Balazinski, M. Fuzzy logic control of industrial heat treatment furnaces. Fuzzy Information Processing Society, 1999. NAFIPS. 18th International Conference of the North American , 1999, 839～843
[36] 杨建刚．人工神经网络实用教程．浙江大学出版社，2001
[37] 袁曾任．人工神经元网络及其应用．清华大学出版社，1999
[38] Albus J.S. A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC). J. of Dynamic System , Measurement and Control , Trans. ASME, 1975, 97 (3): 220～227
[39] Miller W. T., Glanz F.H., Kraft L.G. Application of a general learning algorithm to the control of robotic manipulator. Int. J. Robotics Research, 1987, 6(2): 84～98
[40] Miller W.T. Ⅲ, Hewes R.P. Real time experiments in neural network based learning control during high speed nonrepetitive robotic operations. Intelligent Control, 1988, Proceedings, IEEE International Symposium on , 1989, 513～518
[41] Miller W.T. Ⅲ, Glanz F.H., Kraft L.G. Ⅲ. CMAC: an associative neural network alternative to backpropagation .Proceedings of the IEEE , Volume: 78 Issue: 10 , Oct. 1990 ,1561～1567
[42] Kraft L.G., An E., Campagna D.P. Comparison of CMAC controller weight update laws. Decision and Control, 1989, Proceedings of the 28th IEEE Conference on , 1989, vol. 2, 1746～1747
[43] Kraft L. G., Campagna D. P. Comparison of CMAC architectures for neural network based control. Decision and Control, Proceedings of the 29th IEEE Conference on , 1990, vol. 6 3267～3269
[44] 刘慧等．小脑神经网络反馈学习控制系统．上海交通大学学报，1996，30(4)：114～118
[45] 周旭东，王国栋等．模糊小脑神经网络．自动化学报，1998，24(2)：173～178
[46] Wong Y., Sideris A. Learning convergence in the cerebellar model articulation controller. Neural Networks, IEEE Trans on , Volume: 3 Issue: 1 , Jan. 1992 , 115～121
[2] 张秀凤．我国预应力钢材的现状及发展趋势(续完)．金属制品，2002，28(4)：1～3
[3] 俞勇祥，陈辉明等．感应加热电源的发展．金属热处理，2000(8)：28～29
[4] 林渭勋等．可控硅中频电源．北京：机械工业出版社，1983
[5] 潘天明．工频和中频感应炉，北京：冶金工业出版社，1983
[6] 潘天明．现代感应加热装置．北京：冶金工业出版社，1996
[7] 邵惠鹤．工业过程高级控制，上海交通大学出版社，1996
[8] 金以慧．过程控制，北京：清华大学出版社，1993
[9] 李阳春．自动控制理论在火电厂热工自动化中的应用．[学位论文]．杭州：浙江大学博士论文，2001
[10] Astrom K.J. Toward intelligent control. IEEE Control Systems Magazine , Volume: 9 Issue: 3 , April 1989 : 60～64
[11] 李士勇．模糊控制·神经控制和智能控制论，哈尔滨工业大学出版社，1996
[12] 祁珊，王世栋等．稳定化处理对高碳预应力钢丝性能的影响．热加工工艺，1998(1)：24—25
[13] 王幸之等．单片机应用系统抗干扰技术．北京：北京航空航天大学出版社，2000
[14] 金天均，邵鸣．微机化晶闸管中频电源启动方案设计．电力电子技术，1994，28(1)：14～17
[15] Roffel B., Rijinsdorp J. E.. Process Dynamics. Cntrol and Protection. ANN Arbor Science, 1982
[16] 王骥程．过程动态模型．浙江大学出版社，1994
[17] 丁萃箐．换热设备动态特性计算．北京：水利电力出版社，1993
[18] Ray R, Bowman H.F. A Nonlinear Dynamic Model of a Once-through Subcritical Steam Generator. Trans of the ASME, 1976(9)
[19] Astrom K.J. Autonomous process control. Control Applications, Second IEEE Conference on , vol. 2, 573～580, 1993
[20] 王骥成，祝和云．化工过程控制工程．北京：化学工业出版社，1991
[21] Hang C.C. , Astrom K. J. , Ho W.K. Refinements of the Ziegler-Nichols
tuning formula Control Theory and Applications.. IEE Proceedings D, Vol.138 Issue: 2 , 111～118, March 1991
[22] Ziegler J. G, Nichols N. B. Optimum settings for automatic controllers. Transaction of ASME, 1942, 64:759～768
[23] 王伟，张晶涛，柴天佑，PID参数先进整定方法综述．自动化学报，2000，26(3)：347～355
[24] 陈元杰，汪洋，苏宏业等．串级控制系统的PID参数自动整定算法．控制与决策，1996，11(5)：580～583
[25] Panagopoulos H., Astrom K.J., Hagglund T. A numerical method for design of PI controllers. Control Applications, Proceedings of the 1997 IEEE International Conference on , 1997, 417～422
[26] 薛定宇．反馈控制系统设计与应用—MATLAB语言与应用．清华大学出版社，2000
[27] 诸静．模糊控制原理与应用．北京：机械工业出版社．1996
[28] Kevin M. Passino, Stephen Yurkovich. Fuzzy Control.清华大学出版社，2001
[29] 张乃尧，阎平凡．神经网络与模糊控制．清华大学出版社，1998
[30] Lee C.C. Fuzzy logic in control systems: fuzzy logic controller. Part Ⅰ Ⅱ. Systems, Man and Cybernetics, IEEE Transactions on, Volume: 20 Issue: 2 , 404～435, Mar/Apr 1990
[31] 陶永华．新型PID控制及其应用．北京：机械工业出版社，2000
[32] Liang Xiaoyin , Qu Beimin. Fuzzy-PID controller. TENCON '93. Proceedings, Computer, Communication, Control and Power Engineering, 1993 IEEE Region 10 Conference on Issue: 0,1993 , vol. 4, 296～299
[33] 胡家耀，吴植翘，宋寿山．参数自调整Fuzzy—PI调节器．信息与控制，1987(6)：26～33
[34] Dunlop J. A., Burnham K. J., James D. J.G., King, P.J. Aself-regulating scaling method for fuzzy control .Control Applications, 1994, Proceedings of the Third IEEE Conference on, 24-26 Aug 1994, vol. 1, 683～687
[35] Sobol W., Korwin M. J.,Goraczko M.,Balazinski, M. Fuzzy logic control of industrial heat treatment furnaces. Fuzzy Information Processing Society, 1999. NAFIPS. 18th International Conference of the North American , 1999, 839～843
[36] 杨建刚．人工神经网络实用教程．浙江大学出版社，2001
[37] 袁曾任．人工神经元网络及其应用．清华大学出版社，1999
[38] Albus J.S. A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC). J. of Dynamic System , Measurement and Control , Trans. ASME, 1975, 97 (3): 220～227
[39] Miller W. T., Glanz F.H., Kraft L.G. Application of a general learning algorithm to the control of robotic manipulator. Int. J. Robotics Research, 1987, 6(2): 84～98
[40] Miller W.T. Ⅲ, Hewes R.P. Real time experiments in neural network based learning control during high speed nonrepetitive robotic operations. Intelligent Control, 1988, Proceedings, IEEE International Symposium on , 1989, 513～518
[41] Miller W.T. Ⅲ, Glanz F.H., Kraft L.G. Ⅲ. CMAC: an associative neural network alternative to backpropagation .Proceedings of the IEEE , Volume: 78 Issue: 10 , Oct. 1990 ,1561～1567
[42] Kraft L.G., An E., Campagna D.P. Comparison of CMAC controller weight update laws. Decision and Control, 1989, Proceedings of the 28th IEEE Conference on , 1989, vol. 2, 1746～1747
[43] Kraft L. G., Campagna D. P. Comparison of CMAC architectures for neural network based control. Decision and Control, Proceedings of the 29th IEEE Conference on , 1990, vol. 6 3267～3269
[44] 刘慧等．小脑神经网络反馈学习控制系统．上海交通大学学报，1996，30(4)：114～118
[45] 周旭东，王国栋等．模糊小脑神经网络．自动化学报，1998，24(2)：173～178
[46] Wong Y., Sideris A. Learning convergence in the cerebellar model articulation controller. Neural Networks, IEEE Trans on , Volume: 3 Issue: 1 , Jan. 1992 , 115～121