集装箱起重机防摇的模糊智能控制研究及仿真
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摘要
减小和防止集装箱及吊具的摇摆是提高集装箱起重机装卸效率的一个重要环节。电子防摇是一种主动防摇方式，它将减摇和小车的运行控制结合起来考虑，不依赖于司机的操作经验，可以有效地提高集装箱起重机装卸效率，减轻司机的工作强度，是实现港口装卸机械自动化的大势所趋。
在传统的控制理论中，对某一对象的自动控制，必须对该对象建立精确的数学模型，而集装箱起重机装卸集装箱的运行过程是一个复杂的非线性过程，加之港口经常有风浪等随机因素的影响，很难对系统建立精确的数学模型，即使建立了一些数学模型，也会因为这些模型的复杂性而导致模型的不可解。所以采用传统的控制理论来对其进行控制具有相当大的难度。而智能控制方法却不需要建立精确的数学模型，尤其适合对非线性和不确定性系统的控制，因而利用智能控制防摇是集装箱起重机的电子防摇的一个较好的解决方案。
本文介绍了两种智能控制方法：模糊控制和模糊神经网络控制。针对集装箱起重机的运动特性，提出了应用于集装箱起重机防摇的模糊控制器的设计原则和方法。对于考虑小车运动和吊重摆动的模糊防摇控制方案，控制器的输入一般只取两个输入变量，由于没有考虑位置和摆角的变化，导致控制精度较差。本文对控制器的输入取四个变量，但控制器仍然设计成二维的，这样既提高了控制精度，又简化了计算过程。针对单纯模糊控制中控制规则和隶属函数选取的主观性，利用一种改进的模糊神经网络模型，提出了一种具有清晰的空间结构、良好的自学能力和非线性逼近能力的模糊神经网络控制器的设计方法。
本文采用这两种智能控制方法，对集装箱起重机防摇的模糊智能控制进行了计算机仿真研究。在Visual C++6.0软件开发平台上，通过对集装箱起重机力学模型的数值求解，开发了基于集装箱起重机防摇的模糊控制和模糊神经网络控制仿真软件，在一定程度上实现了集装箱起重机防摇的模糊智能控制仿真。经初步验证，本文提出的模糊智能控制方法具有较好的防摇效果。
In order to promote the loading/unloading efficiency of container crane, it is very important to decrease or avoid the swing of container and spreader. Electric anti-swing is an active method. It does not rely on the experience of the operator, but take the control of both anti-swing and movement of the trolley into account, which makes the loading/unloading more efficient, and alleviates the operator's burden of work. It is a trend to make electric anti-swing into application in ports to realize the automation of loading/unloading.
In conventional cybernetics, a precise mathematical model of some object is essential for automation. But, the process of loading/unloading with container crane is complicate and nonlinear. Also, there would be some affection of stochastic factors (e.g. wind) in ports. It is very difficult to build a precise mathematical model of the system. Or even some model is built, it would probably be impossible to solve because of the complication. That is, it is very difficult to control the anti-swing system by means of conventional cybernetics. Being different from conventional cybernetics, intelligent cybernetics needn't build the precise mathematical model. Especially, it suits the control of nonlinear and uncertain system. Therefore, it is a good scheme to apply intelligent cybernetics to the electric anti-swing of container crane.
Two intelligent control methods, fuzzy control and fuzzy neural network control, are introduced in this thesis. Based on the kinetic characteristic of container crane, the design principle and means of fuzzy controller for the anti-swing of container crane are put forward. Generally, the input of controller adopts two variables as to the scheme considering the movement of trolley and the sway of load. This method does not take the change of position and swing angle into account, and then results in low precision of control. In this thesis, four variables are adopted for the input of controller, while the controller is still designed in term of two dimensions. Thereby, this method not only improves the control precision, but also simplifies the process of calculation. Furthermore, because of the subjectivity of the
selection control rules and membership function in simple fuzzy control, this thesis put forward a design method of fuzzy neural network controller with clear space frame, good ability of self-study and nonlinear approach capacity using an improved fuzzy neural network model.
With these two intelligent control methods, this thesis simulates the fuzzy intelligent control of container crane anti-swing on computer. According to the numeric solving of container crane dynamics model, a simulation software of fuzzy control and fuzzy neural network control
is developed on the platform of Visual C++6.0, based on the anti-swing of container crane. In a certain extent, a fuzzy intelligent control simulation of the anti-swing of container crane is realized. Elementarily proved, a preferable anti-swing effect is obtained with the fuzzy intelligent control methods introduced in this thesis.
在传统的控制理论中，对某一对象的自动控制，必须对该对象建立精确的数学模型，而集装箱起重机装卸集装箱的运行过程是一个复杂的非线性过程，加之港口经常有风浪等随机因素的影响，很难对系统建立精确的数学模型，即使建立了一些数学模型，也会因为这些模型的复杂性而导致模型的不可解。所以采用传统的控制理论来对其进行控制具有相当大的难度。而智能控制方法却不需要建立精确的数学模型，尤其适合对非线性和不确定性系统的控制，因而利用智能控制防摇是集装箱起重机的电子防摇的一个较好的解决方案。
本文介绍了两种智能控制方法：模糊控制和模糊神经网络控制。针对集装箱起重机的运动特性，提出了应用于集装箱起重机防摇的模糊控制器的设计原则和方法。对于考虑小车运动和吊重摆动的模糊防摇控制方案，控制器的输入一般只取两个输入变量，由于没有考虑位置和摆角的变化，导致控制精度较差。本文对控制器的输入取四个变量，但控制器仍然设计成二维的，这样既提高了控制精度，又简化了计算过程。针对单纯模糊控制中控制规则和隶属函数选取的主观性，利用一种改进的模糊神经网络模型，提出了一种具有清晰的空间结构、良好的自学能力和非线性逼近能力的模糊神经网络控制器的设计方法。
本文采用这两种智能控制方法，对集装箱起重机防摇的模糊智能控制进行了计算机仿真研究。在Visual C++6.0软件开发平台上，通过对集装箱起重机力学模型的数值求解，开发了基于集装箱起重机防摇的模糊控制和模糊神经网络控制仿真软件，在一定程度上实现了集装箱起重机防摇的模糊智能控制仿真。经初步验证，本文提出的模糊智能控制方法具有较好的防摇效果。
In order to promote the loading/unloading efficiency of container crane, it is very important to decrease or avoid the swing of container and spreader. Electric anti-swing is an active method. It does not rely on the experience of the operator, but take the control of both anti-swing and movement of the trolley into account, which makes the loading/unloading more efficient, and alleviates the operator's burden of work. It is a trend to make electric anti-swing into application in ports to realize the automation of loading/unloading.
In conventional cybernetics, a precise mathematical model of some object is essential for automation. But, the process of loading/unloading with container crane is complicate and nonlinear. Also, there would be some affection of stochastic factors (e.g. wind) in ports. It is very difficult to build a precise mathematical model of the system. Or even some model is built, it would probably be impossible to solve because of the complication. That is, it is very difficult to control the anti-swing system by means of conventional cybernetics. Being different from conventional cybernetics, intelligent cybernetics needn't build the precise mathematical model. Especially, it suits the control of nonlinear and uncertain system. Therefore, it is a good scheme to apply intelligent cybernetics to the electric anti-swing of container crane.
Two intelligent control methods, fuzzy control and fuzzy neural network control, are introduced in this thesis. Based on the kinetic characteristic of container crane, the design principle and means of fuzzy controller for the anti-swing of container crane are put forward. Generally, the input of controller adopts two variables as to the scheme considering the movement of trolley and the sway of load. This method does not take the change of position and swing angle into account, and then results in low precision of control. In this thesis, four variables are adopted for the input of controller, while the controller is still designed in term of two dimensions. Thereby, this method not only improves the control precision, but also simplifies the process of calculation. Furthermore, because of the subjectivity of the
selection control rules and membership function in simple fuzzy control, this thesis put forward a design method of fuzzy neural network controller with clear space frame, good ability of self-study and nonlinear approach capacity using an improved fuzzy neural network model.
With these two intelligent control methods, this thesis simulates the fuzzy intelligent control of container crane anti-swing on computer. According to the numeric solving of container crane dynamics model, a simulation software of fuzzy control and fuzzy neural network control
is developed on the platform of Visual C++6.0, based on the anti-swing of container crane. In a certain extent, a fuzzy intelligent control simulation of the anti-swing of container crane is realized. Elementarily proved, a preferable anti-swing effect is obtained with the fuzzy intelligent control methods introduced in this thesis.
引文
[1] 蒋国仁，港口起重机械，大连：大连海事大学出版社，1995
[2] 胡宗武、阎以诵，起重机动力学，北京：机械工业出版社，1988
[3] 袁福昌，李国湛等，集装箱装卸搬运机械，武汉：港口装卸杂志社，1988
[4] 胡宗武，工程振动分析基础，上海：上海交通大学出版社，1985
[5] 杨叔子、杨克冲，机械工程控制基础，武汉：华中理工大学出版社，1993
[6] 李士勇，模糊控制·神经控制和智能控制论，哈尔滨：哈尔滨工业大学出版社，1998
[7] 王士同，神经模糊系统及其应用，北京：北京航空航天大学出版社，1998
[8] 王士同，模糊系统、模糊神经网络及应用程序设计，上海：上海科学技术文献出版社，1998
[9] 冯冬青、谢宋和，模糊智能控制，北京：化学工业出版社，1998
[10] 陈元琰，张晓竞。计算机图形学实用技术，2000
[11] 侯俊杰，深入浅出MFC第二版，武汉：华中科技大学出版社，2001
[12] 钱能，C++程序设计教程，北京：清华大学出版社，1999
[13] 刘文智，Visual C++6.0教程，北京：电子工业出版社，2000
[14] 林致来，岸边集装箱起重机机械减摇系统，起重运输机械，1998(8)
[15] 薛朵、李宇成，港口集装箱吊车的建模与模糊控制，机电一体化，2000(3)
[16] 梁承姬、张纪元，机械式减摇机构的动力学模型及其数值计算，上海海运学院学报，2000(4)
[17] 沈李建，集装箱装卸桥吊具电子防摇系统，港口装卸，1998(6)
[18] 郭健明，模糊控制在港口机械上的应用研究，港口装卸，2001(2)
[19] 范其蓬、张晓川、范赣军，模糊控制在集装箱小车电子防摇中的应用，港口装卸，1998(6)
[20] 华克强，桥式吊车模糊防摆技术，中国民航学院学报，2000(3)
[21] 顾必冲，岸边集装箱起重机的发展与展望，港口科技动态，1992(1)
[22] 金耀初、诸静、蒋静坪，神经网络自学习模糊控制及其应用，电工技术学报，1994(4)
[23] 郭建明，新型集装箱起重机防摇控制系统研究，交通科技，2000(6)
[24] 康赐荣，用人工神经网络(ANN)实现模糊控制，电脑与信息技术，2001(3)
[25] 钱学清、张惠侨，抓斗起重机电子控摇系统模型的建立，起重运输机械，1999(5)
[26] 何琪敏，集装箱装卸桥吊具控制系统的改进，港口装卸，1995(4)
[27] 王金诺、程文明等，集装箱起重机刚性减摇系统的动态仿真，铁道学报，1995(1)
[28] 徐保林等，轨道式集装箱龙门起重机的刚性减摇装置，港口装卸，1994(6)
[29] 张景元、张荣顺，基于神经网络的模糊控制规则校正方法研究，山东建材学院学报，1997(3)
[30] 邰克政，基于神经网络实现模糊控制的方法，北方工业大学学报，1997(3)
[31] 黄伟明、吴瑞祥、张燮年，神经网络及模糊控制在飞机防滑刹车系统中的应用，航空学报，2001(4)
[32] 舒东来，人工神经网络——模糊控制研究近况，兵工自动化，1998(1)
[33] Filipic, B./Urbancic, T./Krizman, V. A combined machine learning and genetic algorithm approach to controller design. Engineering Applications of Artificial Intelligence, 1999,12(4):401～409
[34] Ho-Hoon Lee, Sung-Kan Cho. A new fuzzy-logic anti-swing control for industrial three-dimensional overhead cranes. Proc. of the 2001 IEEE International Conference on Robotics and Automation, 2001, Vol.3, 2956～2961
[35] Robb, T. Advanced crane motion control. IEE Colloquium on Industrial Automation and Control, March, 1998
[36] Sung-Kun Cho, Ho-Hoon Lee. An anti-swing control of a 3-dimensional overhead crane. Proc. of the 2000 American Control Conference, 2000,vol.2, 1037～1041
[37] Suzuki, Y. and Yamada, S.-I. and Fujikawa, H. Anti-swing control of the container crane by fuzzy control. Proc. of the IECON'93 International Conference on Industrial Electronics, Control, and Instrumentation. 1993,vol.1, 230～235
[38] Itoh, O. and Migita, H. and Itoh, J.and Irie, Y. Application of fuzzy control to automatic crane operation. Proc. of the IECON'93 International Conference on Industrial Electronics, Control, and Instrumentation, 1993,vol.1, 161～164
[39] Sousa, J. M. and Babuska, R. and Bruijn, P. and Verbruggen, H. B. Comparison of conventional and fuzzy predictive control. Proc. of the Fifth IEEE International Conference on Fuzzy Systems, 1996, vol.3, 1782～1787
[40] Liang, Y. C. and Koh, K. K. Concise anti-swing approach for fuzzy crane control. Electronics Letters, 1997,33(2):167～168
[41] Gutierrez, M. and Soto, R. Fuzzy control of a scale prototype overhead crane. Proc. of the 37th IEEE Conference on Decision and Control, 1998, vol.4, 4266-4268
[42] Itoh, O. and Migita and H. and Itoh, J. and Irie, Y. KF dynamic fuzzy crane system. Proc. of 1995 IEEE International Conference on Fuzzy Systems, 1995, vol.5, 63-64
[43] Kees, M. and Burnham, K.J. and Dunoyer, A. and Tabor, J.H. Modelling and simulation considerations for an industrial crane. International Conference on Simulation '98, 1998
[44] Arai, F. and Yoneda, M. and Fukuda, T. and Miyata, K. and Nailo, T. Operational assistance of the crane system by the interactive adaptation interface. TOKYO: Proc. Of the 4th IEEE International Workshop on Robot and Human Communication, July 1995, 333-338
[45] Huang Youfang , Li Yong, Shi Laide. Study of dockside container crane on fuzzy standard DC drive control. 1997 IEEE International Conference on Intelligent Processing Systems, 1997, vol.1,213-217
[46] Hirasawa, K. and Rui Wu and Ohbayashi, M. and Ning Shao. Universal learning network-based fuzzy system and its application to nonlinear control system. 1996 IEEE International Conference on Systems, Man, and Cybernetics, 1996, vol.2, 1190-1195
[47] Kaur, D. and Shah, K. Visualization in Java, the performance of control system, based on fuzzy inference system, with a front-end built in Visual Basic. IFSA World Congress and 20th NAFIPS International Conference, July 2001, vol.2, 1092-1097
[2] 胡宗武、阎以诵，起重机动力学，北京：机械工业出版社，1988
[3] 袁福昌，李国湛等，集装箱装卸搬运机械，武汉：港口装卸杂志社，1988
[4] 胡宗武，工程振动分析基础，上海：上海交通大学出版社，1985
[5] 杨叔子、杨克冲，机械工程控制基础，武汉：华中理工大学出版社，1993
[6] 李士勇，模糊控制·神经控制和智能控制论，哈尔滨：哈尔滨工业大学出版社，1998
[7] 王士同，神经模糊系统及其应用，北京：北京航空航天大学出版社，1998
[8] 王士同，模糊系统、模糊神经网络及应用程序设计，上海：上海科学技术文献出版社，1998
[9] 冯冬青、谢宋和，模糊智能控制，北京：化学工业出版社，1998
[10] 陈元琰，张晓竞。计算机图形学实用技术，2000
[11] 侯俊杰，深入浅出MFC第二版，武汉：华中科技大学出版社，2001
[12] 钱能，C++程序设计教程，北京：清华大学出版社，1999
[13] 刘文智，Visual C++6.0教程，北京：电子工业出版社，2000
[14] 林致来，岸边集装箱起重机机械减摇系统，起重运输机械，1998(8)
[15] 薛朵、李宇成，港口集装箱吊车的建模与模糊控制，机电一体化，2000(3)
[16] 梁承姬、张纪元，机械式减摇机构的动力学模型及其数值计算，上海海运学院学报，2000(4)
[17] 沈李建，集装箱装卸桥吊具电子防摇系统，港口装卸，1998(6)
[18] 郭健明，模糊控制在港口机械上的应用研究，港口装卸，2001(2)
[19] 范其蓬、张晓川、范赣军，模糊控制在集装箱小车电子防摇中的应用，港口装卸，1998(6)
[20] 华克强，桥式吊车模糊防摆技术，中国民航学院学报，2000(3)
[21] 顾必冲，岸边集装箱起重机的发展与展望，港口科技动态，1992(1)
[22] 金耀初、诸静、蒋静坪，神经网络自学习模糊控制及其应用，电工技术学报，1994(4)
[23] 郭建明，新型集装箱起重机防摇控制系统研究，交通科技，2000(6)
[24] 康赐荣，用人工神经网络(ANN)实现模糊控制，电脑与信息技术，2001(3)
[25] 钱学清、张惠侨，抓斗起重机电子控摇系统模型的建立，起重运输机械，1999(5)
[26] 何琪敏，集装箱装卸桥吊具控制系统的改进，港口装卸，1995(4)
[27] 王金诺、程文明等，集装箱起重机刚性减摇系统的动态仿真，铁道学报，1995(1)
[28] 徐保林等，轨道式集装箱龙门起重机的刚性减摇装置，港口装卸，1994(6)
[29] 张景元、张荣顺，基于神经网络的模糊控制规则校正方法研究，山东建材学院学报，1997(3)
[30] 邰克政，基于神经网络实现模糊控制的方法，北方工业大学学报，1997(3)
[31] 黄伟明、吴瑞祥、张燮年，神经网络及模糊控制在飞机防滑刹车系统中的应用，航空学报，2001(4)
[32] 舒东来，人工神经网络——模糊控制研究近况，兵工自动化，1998(1)
[33] Filipic, B./Urbancic, T./Krizman, V. A combined machine learning and genetic algorithm approach to controller design. Engineering Applications of Artificial Intelligence, 1999,12(4):401～409
[34] Ho-Hoon Lee, Sung-Kan Cho. A new fuzzy-logic anti-swing control for industrial three-dimensional overhead cranes. Proc. of the 2001 IEEE International Conference on Robotics and Automation, 2001, Vol.3, 2956～2961
[35] Robb, T. Advanced crane motion control. IEE Colloquium on Industrial Automation and Control, March, 1998
[36] Sung-Kun Cho, Ho-Hoon Lee. An anti-swing control of a 3-dimensional overhead crane. Proc. of the 2000 American Control Conference, 2000,vol.2, 1037～1041
[37] Suzuki, Y. and Yamada, S.-I. and Fujikawa, H. Anti-swing control of the container crane by fuzzy control. Proc. of the IECON'93 International Conference on Industrial Electronics, Control, and Instrumentation. 1993,vol.1, 230～235
[38] Itoh, O. and Migita, H. and Itoh, J.and Irie, Y. Application of fuzzy control to automatic crane operation. Proc. of the IECON'93 International Conference on Industrial Electronics, Control, and Instrumentation, 1993,vol.1, 161～164
[39] Sousa, J. M. and Babuska, R. and Bruijn, P. and Verbruggen, H. B. Comparison of conventional and fuzzy predictive control. Proc. of the Fifth IEEE International Conference on Fuzzy Systems, 1996, vol.3, 1782～1787
[40] Liang, Y. C. and Koh, K. K. Concise anti-swing approach for fuzzy crane control. Electronics Letters, 1997,33(2):167～168
[41] Gutierrez, M. and Soto, R. Fuzzy control of a scale prototype overhead crane. Proc. of the 37th IEEE Conference on Decision and Control, 1998, vol.4, 4266-4268
[42] Itoh, O. and Migita and H. and Itoh, J. and Irie, Y. KF dynamic fuzzy crane system. Proc. of 1995 IEEE International Conference on Fuzzy Systems, 1995, vol.5, 63-64
[43] Kees, M. and Burnham, K.J. and Dunoyer, A. and Tabor, J.H. Modelling and simulation considerations for an industrial crane. International Conference on Simulation '98, 1998
[44] Arai, F. and Yoneda, M. and Fukuda, T. and Miyata, K. and Nailo, T. Operational assistance of the crane system by the interactive adaptation interface. TOKYO: Proc. Of the 4th IEEE International Workshop on Robot and Human Communication, July 1995, 333-338
[45] Huang Youfang , Li Yong, Shi Laide. Study of dockside container crane on fuzzy standard DC drive control. 1997 IEEE International Conference on Intelligent Processing Systems, 1997, vol.1,213-217
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