基于DSP的拉丝机智能控制系统研究
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摘要
论文针对自聚焦透镜制作中的拉丝工艺生产特点,设计出一种基于DSP的拉丝机智能控制系统。系统以DSP芯片作为硬件控制器载体,将模糊神经网络理论应用于控制系统中,利用激光测径仪作为反馈器件对拉丝电机进行闭环控制。通过拉丝机的智能化改造,实现对纤维丝直径和锥度的自动化控制,改变目前由人工控制拉丝工艺的现状,提高生产效率。
本文主要介绍了系统的智能控制算法、硬件平台、软件系统、实验方法及实验结果分析。在智能控制算法研究中,利用MATLAB神经网络工具箱,设计出针对拉丝电机的模糊神经网络模型。硬件平台以TI公司TMS320LF2407A的DSP芯片为核心,设计了电平转换电路、复位电路、时钟电路、SCI串行接口电路、JTAG接口电路以及AD调理电路等部分,为下一步软件设计提供了物理平台。软件系统包括在CCS环境下的DSP控制程序设计以及基于C++ Builder6的上位机界面设计。DSP软件控制程序主要包括:模糊神经网络算法设计,AD转换,串行通讯以及电机控制程序设计。上位机界面设计由主控制界面及各个参数设置的子界面设计组成。论文详细介绍了各个部分设计的思路、实现和检测方法。经过实验室检测各个模块运行正常后,应用于拉丝机在线控制系统,分析实验结果得到设计满足现场拉丝工艺指标要求。
本论文设计的拉丝机智能控制系统无需建立精确数学模型。在应用时,只需将熟练操作人员的经验值作为样本输入模糊神经网络模型离线训练网络参数,将该网络参数应用于在线控制,通过输入反馈量计算出相应的输出控制量。该系统有效减少不确定因素对拉丝工艺的影响,具有良好的实用性和推广价值。
Aiming at improving the pulling technology of self-focus lens during their manufacturing, a pulling machine intelligent control system based on DSP has been developed. While the DSP is integrated as hardware controller, fuzzy neutral network theorie is applied in the control system, and close loop control on the pulling machine is realized by feedback component of laser calliper. Through the intelligentizing on the pulling machine, the automatic control on fibre diameter and conicity is implemented, which would change the current situation of manual work on the pulling technology and improve productivity greatly.
This thesis mainly introduces the system's intelligent control algorithm, hardware platform, software system, experimental methods and result data analysis. During the control algorithm study, the fuzzy neutral network model for the control on the pulling machine motor is designed using MATLAB neutral network toolbox.The key part of hardware design is the DSP model TMS320LF2407A from TI, based on which the level converter circuit, reset circuit, clock circuit, SCI serial interface circuit, JTAG interface circuit and AD modulation circuit are designed. This provides a physical foundation for software design. The software system includes the DSP control program design under CCS environment and the upper-end computer software interface design by C++ Builder 6. The DSP programs comprise: fuzzy neutral network algorithm design, AD conversion program design, serial communication design and motor control program design. The upper-end computer interface consists of the main control interface and other sub-interfaces for parameter setting. The design methods, its realization and the detection techniques are introduced in details in the thesis. All the function modules have been working properly after the laboratory experiment and then the system has been applied on the pulling machine in the field. The analysis results meet the requirement of fibre pulling standards.
The pulling machine intelligent control system developed in this thesis need not precise mathematical model. For application, only the offline results from experienced operator are needed as input of the fuzzy neutral network model. After the parameters are determined and the online feedback value is known, the control value will be outputted by the network calculation. The system efficiently decreases the influence of unknown factors on the pulling procedure, and has a good practicability and commercial importance.
本文主要介绍了系统的智能控制算法、硬件平台、软件系统、实验方法及实验结果分析。在智能控制算法研究中,利用MATLAB神经网络工具箱,设计出针对拉丝电机的模糊神经网络模型。硬件平台以TI公司TMS320LF2407A的DSP芯片为核心,设计了电平转换电路、复位电路、时钟电路、SCI串行接口电路、JTAG接口电路以及AD调理电路等部分,为下一步软件设计提供了物理平台。软件系统包括在CCS环境下的DSP控制程序设计以及基于C++ Builder6的上位机界面设计。DSP软件控制程序主要包括:模糊神经网络算法设计,AD转换,串行通讯以及电机控制程序设计。上位机界面设计由主控制界面及各个参数设置的子界面设计组成。论文详细介绍了各个部分设计的思路、实现和检测方法。经过实验室检测各个模块运行正常后,应用于拉丝机在线控制系统,分析实验结果得到设计满足现场拉丝工艺指标要求。
本论文设计的拉丝机智能控制系统无需建立精确数学模型。在应用时,只需将熟练操作人员的经验值作为样本输入模糊神经网络模型离线训练网络参数,将该网络参数应用于在线控制,通过输入反馈量计算出相应的输出控制量。该系统有效减少不确定因素对拉丝工艺的影响,具有良好的实用性和推广价值。
Aiming at improving the pulling technology of self-focus lens during their manufacturing, a pulling machine intelligent control system based on DSP has been developed. While the DSP is integrated as hardware controller, fuzzy neutral network theorie is applied in the control system, and close loop control on the pulling machine is realized by feedback component of laser calliper. Through the intelligentizing on the pulling machine, the automatic control on fibre diameter and conicity is implemented, which would change the current situation of manual work on the pulling technology and improve productivity greatly.
This thesis mainly introduces the system's intelligent control algorithm, hardware platform, software system, experimental methods and result data analysis. During the control algorithm study, the fuzzy neutral network model for the control on the pulling machine motor is designed using MATLAB neutral network toolbox.The key part of hardware design is the DSP model TMS320LF2407A from TI, based on which the level converter circuit, reset circuit, clock circuit, SCI serial interface circuit, JTAG interface circuit and AD modulation circuit are designed. This provides a physical foundation for software design. The software system includes the DSP control program design under CCS environment and the upper-end computer software interface design by C++ Builder 6. The DSP programs comprise: fuzzy neutral network algorithm design, AD conversion program design, serial communication design and motor control program design. The upper-end computer interface consists of the main control interface and other sub-interfaces for parameter setting. The design methods, its realization and the detection techniques are introduced in details in the thesis. All the function modules have been working properly after the laboratory experiment and then the system has been applied on the pulling machine in the field. The analysis results meet the requirement of fibre pulling standards.
The pulling machine intelligent control system developed in this thesis need not precise mathematical model. For application, only the offline results from experienced operator are needed as input of the fuzzy neutral network model. After the parameters are determined and the online feedback value is known, the control value will be outputted by the network calculation. The system efficiently decreases the influence of unknown factors on the pulling procedure, and has a good practicability and commercial importance.
引文
[1]D.T.Moore.Gradient-Index Lenses Technology Derived Form Nature[J].Am.Cer.Soc Bull,1989,68(11):1941-1945.
[2]M.C.W.Campbell.Measurement of Refractive-lndex in an Intact Crystalline Lens[J].Vision Research,1984,24(5):409.
[3]W.S.Jagger and P.J.Stands.A wide-angle gradient index optical model of crystalline lens and eye of the ocptopus[J].Vision Research,1999,39(17):2481-2452.
[4]J.C.Maxwell.Solution of problems[J].Cambridge and Dublin Math.J.1854.8:188.
[5]T.Uchida,M.Furukawa,I.Kitano,K.Koizumi and H.Matsumura,A light-focusing fiber guide[J].IEEE Jour.Q.E.(Digest of Technical Papers),1969,QE-5,331.
[6]龚智炳.自聚焦透镜列阵及其应用[J].应用光学,2000,21(1):28-31.
[7]刘德森.光电子器件新技术讨论[C].到2020年中国通信科技发展方向及相关政策研讨会讨论集,2003,116-123
[8]周自刚,刘德森,刘晓东.铊玻璃自聚焦透镜边缘折射率分布的改进[J].光子学报,1999,28(10):943-946.
[9]Hamanaka K,Nemoto H.Design of a new multiplexer-demultiplexere by the use of planar micro lenses[J].Appl Opt,1994,33(16):3415-3419.
[10]Hashiume H,Hamanaka K,Graham A C,et al.The future of gradient index optics[J].Proc SPIE,2001,4437:26-39.
[11]Raguin D H,Chakmakjian S,Farmiga N,et al.Collimator arrays for telecommunication length applications[J].Proc of SPIE,4437:116-124.
[12]梅锁海.球形自聚焦平面微透镜阵列[D].西安:西安光机所,1991
[13]刘德森,胡建明,刘炜等.平面交叉型微透镜阵列的制作及成像特性研究[J].中国激光,2005,32(6):743-748.
[14]Bar J,Brenner K H.Realization of refractive continuous phase elements with high design freedom mask structuredion exchange[J].Proc of SPIE,2001,4437:50-59.
[15]Tomlinson W J,Applications of GRIN rod lens in optical fiber communication systems[J].Appl.Opt.1980,19,1127.
[16]T.Miyazawa,K.Okada,T.Kubo et al.Appl.Opt,1980,19,1113.
[17]J.M.Legeais,P.Rol,B.Briat et al.Microendoscope with gradient-index lenses,First clinical evaluation[J],J.Francais D Ophtalmologie,1997,20(6),439-443.
[18]张乃尧,阎平凡.神经网络与模糊控制[M].北京:清华大学出版社,2002:217-235.
[19]王洪元,史国栋.人工神经网络技术及其应用[M].北京:高等教育出版社,2001
[20]朱大奇,史慧.人工神经网络原理及应用[M].北京:科技出版社,2006:1-29.
[21]刘增良 刘有才.模糊逻辑与神经网络-理论研究与探索[M]北京:北京航空航天大学出版社,1996:213-218
[22]张吉礼.模糊-神经网络控制原理与工程应用[M].哈尔滨:哈尔滨工业大学出版社,2004.
[23]吴冬梅,张玉杰.DSP技术及应用[M].北京:北京大学出版社,2006:1-9.
[24]刘德森,高应俊.变折射率介质的物理基础[M].北京:国防工业出版社,1991:156.
[25]张曾科.模糊数学在自动化技术中的应用[M].北京:清华大学出版社,1997:2-4.
[26]Passino,K.M.Bridging the Gap between Conventional and Intelligent Control[J].IEEE Control Systems Magazine,1993,13(2):12-18.
[27]刘曙光等.模糊控制技术[M].北京:中国纺织出版社,2001:4-6.
[28]Euntai Kim,Heejin Lee.New approaches to relaxed quadratic stability condition of fuzzy control systems[J].IEEE Transaction on Fuzzy Systems,2000,8(5):523-534.
[29]Wu.J.C.T.S.Fuzzy control stabilization with application to motorcycle control systems[J].Man and Cybernetics,1996,26(26):836-847.
[30]陈明.神经网络模型[M].大连:大连理工大学出版社.1995:1-6.
[31]Fredric M.Ham Ivica Kostanic著 叶世伟 王海娟 译.Principles of Neurocpmputing for Science &Engineering神经网络原理[M].北京:机械工业出版社.2007:45-50.
[32]赵振宇 徐用懋.模糊理论和神经网络的基础与应用[M].北京:清华大学出版社 广西科学技术出版社,1996:165-172.
[33]李士勇,模糊控制·神经控制和智能控制论[M].哈尔滨:哈尔滨工业大学出版社,1996:427-443.
[34]王沫然.MATLBA与科学计算[M].北京:电子工业出版社,2003:169-176
[35]张国良,曾静,柯熙政,邓方林.模糊控制及其MATLA应用[M].西安:西安交通大学出版社,2002:47-56.
[36]飞思科技产品研发中心.神经网络理论与MATLAB 7实现[M].北京:电子工业出版社,2005:299-311.
[37]王念旭等.DSP基础与应用系统设计[M].北京:北京航空航天大学出版社,2001:2-13.
[38]陈金鹰.DSP技术及应用[M].北京:机械工业出版社,2004:13-15.
[39]Texas Instruments.TMS320/LC240xA DSP Controllers Reference Guild-Systems and Peripherals SPRU357.December,2001.
[40]章云,谢莉萍,熊红艳.DSP控制器及其应用[M].北京:机械工业出版社.2001:12-34.
[41]刘和平,邓力,江渝,郑群英.DSP原理及电机控制应用[M].北京:北京航空航天大学出版社.2006:1-12
[42]汪安民,程昱,徐保根.DSP嵌入式系统开发典型案例[M].北京:人民邮电出版社出版发行.2007:89-94.
[43]Texas Instruments.Low-Dropout Voltage Regulators With Integrated Delayed Reset Function.1999.
[44]Maxim Integrated Products.Low-Cost,uP Supervisory Circuits.2005.
[45]苏涛,蔺丽华,卢光跃,张林让.DSP实用技术[M].西安:西安电子科技大学出版社.2002:244-247.
[46]Texas Instruments.Code Composer Studio Getting Started Guide.SPRU509C.November,2001.
[47]Texas Instruments.TMS320F/C2x/C2xx/C5x Assembly Language Tools User's Guide.SPRU018D.Match,1995.
[48]Texas Instruments.Space-Vector PWM With TMS320C24x/F23x Using Hardware and Software Determined Switching Patterns.SPRA524.
[49]席卫文,张春晓,李光明.C++ Builder 6程序设计与实例[M].北京:冶金工业出版社.2003.
[50]范逸之,江文贤,陈立元.C++ Builder与RS-232串行通信控制[M].北京:清华大学出版社.2002
[51]Jan Axelson.Serial Port Complete-Programming and Circuits for RS-232 and RS-485 Links and Networks[M].Lakeview Research.1999:13-24.
[2]M.C.W.Campbell.Measurement of Refractive-lndex in an Intact Crystalline Lens[J].Vision Research,1984,24(5):409.
[3]W.S.Jagger and P.J.Stands.A wide-angle gradient index optical model of crystalline lens and eye of the ocptopus[J].Vision Research,1999,39(17):2481-2452.
[4]J.C.Maxwell.Solution of problems[J].Cambridge and Dublin Math.J.1854.8:188.
[5]T.Uchida,M.Furukawa,I.Kitano,K.Koizumi and H.Matsumura,A light-focusing fiber guide[J].IEEE Jour.Q.E.(Digest of Technical Papers),1969,QE-5,331.
[6]龚智炳.自聚焦透镜列阵及其应用[J].应用光学,2000,21(1):28-31.
[7]刘德森.光电子器件新技术讨论[C].到2020年中国通信科技发展方向及相关政策研讨会讨论集,2003,116-123
[8]周自刚,刘德森,刘晓东.铊玻璃自聚焦透镜边缘折射率分布的改进[J].光子学报,1999,28(10):943-946.
[9]Hamanaka K,Nemoto H.Design of a new multiplexer-demultiplexere by the use of planar micro lenses[J].Appl Opt,1994,33(16):3415-3419.
[10]Hashiume H,Hamanaka K,Graham A C,et al.The future of gradient index optics[J].Proc SPIE,2001,4437:26-39.
[11]Raguin D H,Chakmakjian S,Farmiga N,et al.Collimator arrays for telecommunication length applications[J].Proc of SPIE,4437:116-124.
[12]梅锁海.球形自聚焦平面微透镜阵列[D].西安:西安光机所,1991
[13]刘德森,胡建明,刘炜等.平面交叉型微透镜阵列的制作及成像特性研究[J].中国激光,2005,32(6):743-748.
[14]Bar J,Brenner K H.Realization of refractive continuous phase elements with high design freedom mask structuredion exchange[J].Proc of SPIE,2001,4437:50-59.
[15]Tomlinson W J,Applications of GRIN rod lens in optical fiber communication systems[J].Appl.Opt.1980,19,1127.
[16]T.Miyazawa,K.Okada,T.Kubo et al.Appl.Opt,1980,19,1113.
[17]J.M.Legeais,P.Rol,B.Briat et al.Microendoscope with gradient-index lenses,First clinical evaluation[J],J.Francais D Ophtalmologie,1997,20(6),439-443.
[18]张乃尧,阎平凡.神经网络与模糊控制[M].北京:清华大学出版社,2002:217-235.
[19]王洪元,史国栋.人工神经网络技术及其应用[M].北京:高等教育出版社,2001
[20]朱大奇,史慧.人工神经网络原理及应用[M].北京:科技出版社,2006:1-29.
[21]刘增良 刘有才.模糊逻辑与神经网络-理论研究与探索[M]北京:北京航空航天大学出版社,1996:213-218
[22]张吉礼.模糊-神经网络控制原理与工程应用[M].哈尔滨:哈尔滨工业大学出版社,2004.
[23]吴冬梅,张玉杰.DSP技术及应用[M].北京:北京大学出版社,2006:1-9.
[24]刘德森,高应俊.变折射率介质的物理基础[M].北京:国防工业出版社,1991:156.
[25]张曾科.模糊数学在自动化技术中的应用[M].北京:清华大学出版社,1997:2-4.
[26]Passino,K.M.Bridging the Gap between Conventional and Intelligent Control[J].IEEE Control Systems Magazine,1993,13(2):12-18.
[27]刘曙光等.模糊控制技术[M].北京:中国纺织出版社,2001:4-6.
[28]Euntai Kim,Heejin Lee.New approaches to relaxed quadratic stability condition of fuzzy control systems[J].IEEE Transaction on Fuzzy Systems,2000,8(5):523-534.
[29]Wu.J.C.T.S.Fuzzy control stabilization with application to motorcycle control systems[J].Man and Cybernetics,1996,26(26):836-847.
[30]陈明.神经网络模型[M].大连:大连理工大学出版社.1995:1-6.
[31]Fredric M.Ham Ivica Kostanic著 叶世伟 王海娟 译.Principles of Neurocpmputing for Science &Engineering神经网络原理[M].北京:机械工业出版社.2007:45-50.
[32]赵振宇 徐用懋.模糊理论和神经网络的基础与应用[M].北京:清华大学出版社 广西科学技术出版社,1996:165-172.
[33]李士勇,模糊控制·神经控制和智能控制论[M].哈尔滨:哈尔滨工业大学出版社,1996:427-443.
[34]王沫然.MATLBA与科学计算[M].北京:电子工业出版社,2003:169-176
[35]张国良,曾静,柯熙政,邓方林.模糊控制及其MATLA应用[M].西安:西安交通大学出版社,2002:47-56.
[36]飞思科技产品研发中心.神经网络理论与MATLAB 7实现[M].北京:电子工业出版社,2005:299-311.
[37]王念旭等.DSP基础与应用系统设计[M].北京:北京航空航天大学出版社,2001:2-13.
[38]陈金鹰.DSP技术及应用[M].北京:机械工业出版社,2004:13-15.
[39]Texas Instruments.TMS320/LC240xA DSP Controllers Reference Guild-Systems and Peripherals SPRU357.December,2001.
[40]章云,谢莉萍,熊红艳.DSP控制器及其应用[M].北京:机械工业出版社.2001:12-34.
[41]刘和平,邓力,江渝,郑群英.DSP原理及电机控制应用[M].北京:北京航空航天大学出版社.2006:1-12
[42]汪安民,程昱,徐保根.DSP嵌入式系统开发典型案例[M].北京:人民邮电出版社出版发行.2007:89-94.
[43]Texas Instruments.Low-Dropout Voltage Regulators With Integrated Delayed Reset Function.1999.
[44]Maxim Integrated Products.Low-Cost,uP Supervisory Circuits.2005.
[45]苏涛,蔺丽华,卢光跃,张林让.DSP实用技术[M].西安:西安电子科技大学出版社.2002:244-247.
[46]Texas Instruments.Code Composer Studio Getting Started Guide.SPRU509C.November,2001.
[47]Texas Instruments.TMS320F/C2x/C2xx/C5x Assembly Language Tools User's Guide.SPRU018D.Match,1995.
[48]Texas Instruments.Space-Vector PWM With TMS320C24x/F23x Using Hardware and Software Determined Switching Patterns.SPRA524.
[49]席卫文,张春晓,李光明.C++ Builder 6程序设计与实例[M].北京:冶金工业出版社.2003.
[50]范逸之,江文贤,陈立元.C++ Builder与RS-232串行通信控制[M].北京:清华大学出版社.2002
[51]Jan Axelson.Serial Port Complete-Programming and Circuits for RS-232 and RS-485 Links and Networks[M].Lakeview Research.1999:13-24.