系统辨识大牛Ljung编写的MATLAB系统辨识使用手册
系统辨识大牛Ljung编写的MATLAB系统辨识使用手册，这本书详细地介绍了在MATLAB已经所属simulink环境下，系统辨识工具箱的一些使用办法，是一本非常经典的教材！Revision Historypril 1988First printingJuly 1991Second printingMay1995Third printingNovember 2000 Fourth printingRevised for Version 5.0(Release 12)pril 2001Fifth printingJuly 2002Online onlyRevised for Version 5.0.2 Release 13)June 2004Sixth printingRevised for Version 6.0.1(Release 14)March 2005Online onlyRevised for Version 6.1.1Release 14SP2)September 2005 Seventh printingRevised for Version 6.1.2(Release 14SP3)March 2006Online onlyRevised for Version 6.1.3(Release 2006a)September 2006 Online onlyRevised for Version 6.2 Release 2006b)March 2007Online onlyRevised for Version 7.0 ( Release 2007a)September 2007 Online onlyRevised for Version 7.1 (Release 2007bMarch 2008Online onlyRevised for Version 7.2(Release 2008a)October 2008Online onlyRevised for Version 7.2.1 Release 2008b)March 2009Online onlyRevised for Version 7.3(Release 2009a)September 2009 Online onlyRevised for Version 7.3.1(Release 2009b)March 2010Online onlyRevised for Version 7. 4 (Release 2010a)eptember2010 Online onlyRevised for Version 7.4.1(Release 2010b)pril 2011Online onlRevised for Version 7.4.2(Release 2011a)September 2011 Online onlyRevised for Version 7.4.3(Release 2011b)March 2012Online onlyRevised for Version 8.0( Release 2012aabout the DevelopersAbout the Developersystem Identification Toolbox software is developed in association with thefollowing leading researchers in the system identification fieldLennart Ljung. Professor Lennart Ljung is with the department ofElectrical Engineering at Linkoping University in Sweden. He is a recognizedleader in system identification and has published numerous papers and booksin this areaQinghua Zhang. Dr. Qinghua Zhang is a researcher at Institut Nationalde recherche en Informatique et en Automatique(INria) and at Institut deRecherche en Informatique et systemes Aleatoires (Irisa), both in rennesFrance. He conducts research in the areas of nonlinear system identificationfault diagnosis, and signal processing with applications in the fields of energyautomotive, and biomedical systemsPeter Lindskog. Dr. Peter Lindskog is employed by nira dynamiAB, Sweden. He conducts research in the areas of system identificationsignal processing, and automatic control with a focus on vehicle industryapplicationsAnatoli Juditsky. Professor Anatoli Juditsky is with the laboratoire JeanKuntzmann at the Universite Joseph Fourier, Grenoble, france. He conductsresearch in the areas of nonparametric statistics, system identification, andstochastic optimizationAbout the developersContentsChoosing Your System Identification ApproachLinear model structures1-2What Are Model objects?Model objects represent linear systemsAbout model data1-5Types of Model objectsDynamic System Models1-9Numeric Models1-11umeric Linear Time Invariant (LTD Models1-11Identified LTI modelsIdentified Nonlinear models1-12Nonlinear model structures1-13Recommended Model Estimation Sequence1-14Supported Models for Time- and Frequency-DomainData,,,,,,,1-16Supported Models for Time-Domain Data1-16Supported Models for Frequency-Domain Data1-17See also1-18Supported Continuous-and Discrete-Time Models1-19Model estimation commands1-21Creating Model Structures at the command Line ... 1-22about system Identification Toolbox Model Objects ... 1-22When to Construct a Model Structure Independently ofEstimation1-23Commands for Constructing Model Structures1-24Model Properties1-25See als1-27Modeling Multiple-Output Systems ......... 1-28About Modeling multiple-Output Systems1-28Modeling Multiple Outputs Directly1-29Modeling multiple outputs as a Combination ofSingle-Output Models.......1-29Improving Multiple-Output Estimation Results byWeighing Outputs During Estimation ....... 1-30Identified linear Time-Invariant models1-32IDLTI Models1-32Configuration of the Structure of Measured and Noise oRepresentation of the Measured and noise Components foVarious model Types1-33Components ....1-35Imposing Constraints on the Values of ModeParameters1-37Estimation of Linear models1-8Data Import and Processing2「Supported Data ...2-3Ways to Obtain Identification DataWays to Prepare Data for System Identification ... 2-6Requirements on Data SamplingRepresenting Data in MATLAB Workspace·····Time-Domain Data Representation2-9Time-Series Data Representation2-10ContentsFrequency-Domain Data Representation ....... 2-11Importing Data into the Gui2-17Types of Data You Can import into the GUi2-17Importing time-Domain Data into the GUI2-18Importing Frequency-Domain Data into the GUI2-22Importing Data Objects into the GUI ......... 2-30Specifying the data sampling interval2-34Specifying estimation and validation Data2-35Preping data Using Quick StartCreating Data Sets from a Subset of Signal Channelo2-362-37Creating multiexperiment Data Sets in the gUi2-39Managing data in the gui ............. 2-46Representing Time- and Frequency-Domain Data Usingiddata object2-55iddata constructor2-55iddata Properties.........2-58Creating Multiexperiment Data at the Command Line .. 2-61Select Data Channels, I/O Data and Experiments in iddataObjects2-63Increasing Number of Channels or Data Points of iddataObjects2-67Managing iddata Objects2-69Representing Frequency-Response Data Using idfrdObiec2-76idfrd Constructor2-76idfrd Properties2-77Select I/o Channels and Data in idfrd Objects ..... 2-79Adding Input or Output Channels in idfrd Objects2-80Managing idfrd Objects2-83Operations That Create idfrd Objects2-83Analyzing Data quality2-85Is your data ready for modeling?2-85Plotting Data in the guI Versus at the command line2-86How to plot data in the gui2-86How to plot data at the command line2-92How to Analyze Data Using the advice Command2-94Selecting Subsets of Data2-96IXWhy Select Subsets of Data?2-96Extract Subsets of Data Using the GUI2-97Extract Subsets of data at the Command Line2-99Handling Missing Data and outliers2-100Handling missing data2-100Handling outliers2-101Extract and Model Specific Data Segments2-102See also2-103Handling offsets and Trends in Data2-104When to detrend data2-104Alternatives for Detrending Data in GUi or at theCommand-Line2-105Next Steps After detrending2-107How to Detrend Data Using the Gui2-108How to detrend data at the Command line2-109Detrending Steady-State Dat109cending transient Dat2-109See also2-110Resampling Data2-111What Is resampling?...,,.,,,,,,,,,,,.2-111Resampling data without Aliasing Effects2-112See also2-116Resampling data Using the GUi.,,,,2-117Resampling Data at the Command line2-118Filtering Data2-120Supported Filters2-120Choosing to Prefilter Your Data2-120See also2-121How to Filter Data Using the gui2-122Filtering Time-Domain Data in the GuI........ 2-122Content
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agv的plc控制
这个文档主要介绍plc的整套对agv的开发过程对现实还是有一定的积极意义的长沙理工大学学位论文原创性声明本人郑重声明:所呈交的论文是本人在导师的指导下独立进行研究所取得的研究成果。除了文中特别加以标注引用的内容外,本论文不包含任何其他个人或集体已经发表或撰写的成果作品。对本文的研究做出重要贡献的个人和集体,均已在文中以明确方式标明。本人完全意识到本声明的法律后果由本人承担。作者签名:6日期:20/b月&日学位论文版权使用授权书本学位论文作者完全了解学校有关保留、使用学位论文的规定,同意学校保留并向国家有关部门或机构送交论文的复印件和电子版,允许论文被查阅和借阅。本人授权长沙理工大学可以将本学位论文的全部或部分内容编入有关数据库进行检索,可以采用影印、维印或扫描等复制手段保存和汇编本学位论文。同时授权中国科学技术信息研究所将本论文收录到《中国学位论文全文数据库》,并通过网络向社会公众提供信息服务。本学位论文属于1、保密口,在年解密后适用本授权书。2、不保密駟。(请在以上相应方框内打“√”)作者签名:日期:0/年6月孑日导师签名:日期:年/月。日摘要AGV自动导航车是现代物流领域的一个重要的研究课题。在工业领域柔性化生产中越来越重要,高性能、高有效性的AGV控制器受到国内外学者的高度重视。本文采用模糊控制方式,针对AGⅴ小车的响应速度、稳定性要求,设计实现了一种基于运动学模型的AGV控制系统。该控制系统按功能模块化设计硬件电路,由电杌驱动模块、路径识别模块、避障模块、通信模块组成。电机驱动模块由直流无刷电机驱动模块和电机速度检测模块组成,实现对直流无刷电机的控制;路径识别模块由位置检测模块和站点识別模块组成,实现小车对路径的识别,达到对路径跟踪控制的目的;避障模块实现小车的安全性;通信模块由CAN通信模块和无线ⅵi模块组成,实现控制系统的通信。小车的测试实验表明,先后给以80mm和-100mm的位置偏差,AGV小车能够在3s内对位置偏差进行修复,小车速度振幅控制在±10mm/s,小车位置偏差修复后,3s时间内,速度可回调到位置偏差前的速度,即小车回“零位”的速度比较快,表明AGⅴ控制系统的平稳性好,能够很好实现对路径的跟踪,同时也能及时的应对运行过程中的误差突变。关键字:AG;运动学模型;磁导航;模糊控制ABSTRACTThe Automatic Guided Vehicle (AGv) is an important research topic in the fieldof modern logistics. Flexibility in the field of industrial production more and moreimportant, the AGV controller which has the high performance and the highefficiency has been received the extensive attention by scholars both at home andabroad. This paper adopts the fuzzy control method, in view of the aGv car responsespeed and stability requirements, the design has realized one kind of AGv controlsystem based on kinematics modelThe control system hardware circuit is designed according to the functionmodular, the motor drive module, path recognition module, obstacle avoidancemodule, communication module. Motor driver module is composed of brushless dcmotor driver module and motor speed detection module, realize the control for thebrushless dc motor. Path recognition module is composed of position detectionmodule and site identification module, realize the aGv for the path recognition,achieve the goal of the path tracking control; Obstacle avoidance module to realizethe safety of the car; Communication module of Can communication module andwireless wifi module composition, realization of the control system ofcommunicationThe test results show that, successively give 80mm and 100mm positionaldeviation. the location of the AGv can within 3s to fix position deviation, vehiclespeed amplitude control in the plus or minus 10mm/s after repair the car positiondeviation, within 3s the speed can be back to the front of the position deviation ofspeed, the car back to " zero"speed is faster, the results show that the AGv controlsystem have good stability, a good path tracking, and can provide timely responsesduring the operation of the error mutationKeywords: AGV; Kinematics Model; Magnetic Navigation; Fuzzy Control目录摘要ABSTRACT…命命命中苹命命品哪命命命哪请自品中“非哪非命命命哪第一章绪论1AGV概述…12AGV国内外发展现状…121国外发展历史及现状122国内发展历史及现状13AGV的引导方式14本文的主要工作和主要问题.b自.身自命看非世看带看音萨世看中·宁非命●学●122455141本论文的主要工作…142本论文解决的主要问题第二章AGV车体结构及运动学模型建立21AGV结构分类。B西自鲁自·切是息甲带导211三轮结构2.12四轮结构213五轮或六轮结构.22AGV的性能指标23AGⅤ的组成幽·命命66778924AGⅤ运动状态分析申·自意25AGⅤ小车对电机的基本要求1026AGV的运动学模型27本章小结12第三章AGNV控制系统硬件设计3IAGⅴ控制系统整体结构.1332AGV控制系统研究方案32ICAN总线通信模块即14322无线wif模块15323电机实时速度检测看·。专中●·音专中324磁导航传感器组3.2.5障碍检测18326AGV小车对线路与站点的识别鲁·。感垂品自世品身岳一益自备自e非你ee非物9327AGV小车位置判断33硬件系统组成21331控制器芯片应用说明.1332电机驱动模块24333电源模块…633.4通讯模块命命命命··命命自命命●2734本章小结28第四章AGV路径跟踪控制方法研究41AGⅴ控制策略选择42模糊控制简介…294.3模糊PD控制器.3043.1模糊PD控制器的数学模型查看着看看看昏春●●●43,2模糊PID控制器的控制思想3143.3AGⅴ控制系统模糊控制必要性…3144模糊PID控制器设计…3344.1模糊PID控制器的输入输出量的确定…344.2模糊控制规则的设计.节。申春。鲁合。节是看看。节.自DD春3345模糊PID控制器的仿真…昨非···命e命自···总最46本章小结38第五章AGV控制系统软件设计51引言3952编译环境的介绍.....12053控制软件设计鲁暴非画非最命自曲曲自自非非命春告音春鲁D看41531产生PWM的程序流程.53,2电机实际速度检测模块程序流程425.3.3避障控制模块程序流程….534驱动控制模块程序流程中学鲁鲁鲁。鲁。●44535CAN总线通信模块程序流程.54本章小结45第六章系统测试与结果分析6.1路径跟踪测试.4662避障能力测试63本章小结总结和展望看中·49参考文献看●。。意非。。市中自看非如鲁致谢附录A(攻读学位期间发表论文及专利目录)55第一章绪论第一章绪论1.1AGV概述现代制造工艺的飞速发展,带动了柔性制造系统FMS和柔性装配系统FAS的迅速发展。中国早在“十一五”规划中就制定了侧重于科学和技术的发展,以先进的制造技术来提高企业的竞争力。自21世纪开始,物流的发展成为一个新热点。现代物流行业,尤其是西方的设备和实现技术已经达到很成熟的水平。目前,现代化物流格局已经形成以信息技术为核心,以信息、运送、卸载、自主化仓储、库存统计、自主化配货、包装等专业技术为支撑的现代化物流技术4。而自主导航车AGV是实现AGVS、FMS、CIMs的关键基础设备,是实现现代物流自动化和智能化的核心技术之—pAGV( Automated Guided Vehicle)是自动导航小车的英文缩写,是一种自主驾驶、无人操纵、以电池为动力的自动化运输设备,装有电磁或者光学等非接触自动导向装置和独立寻址系统。它的主要特点表现为具有可编程功能、安全保护装置、启停装置以及搬载功能并能在上位机的监控下,根据给定的起点和终点自主地沿预设的导引路径行驶,安全到达目的地,完成搬运卸载任务。其已经成为仓储物流自动化系统、柔性生产线、柔性装配线的重要设备。资料显示,在整个产品生产的过程中,用于加工和制造仅仅只占有5%的时间,剩余95%的时间主要用于包装、储存、装卸和运输;而在美国、日本和欧洲发达国家,直接劳动成本所占生产成本的比例不足15%而且这一比例还在不断下降,而储存、运输所占的成本却占总成本的40%7。因此各工业强国把降低物流成本作为提高企业竞争力的重要措施,在这样的背景下,AGⅤ小车广泛地应用于各行各业,并受到了极大的欢迎。12AG国内外发展现状AGⅤ是伴随着叉车技术和机器人技术产生并发展而来的,但都是为了实现货物的自动搬运为目的的3。随着技术的不断发展,AGV的功能不断完善,应用领域越来越大。硕学位论文121国外发展历史及现状AGV在国外起步早、发展快。首次出现在公众视野的AGV原型车于1913在美国福特汽车公司下线,该原型车首次将有轨引导的小车代替原来使用在汽车底盘装配线上的输送机,根据福特公司对外公开的资料,该小车将装配时间缩短了15小时,极大地提高了生产效率,从此AGv就步入了高速发展的时代例1953年,世界上第一台AGV由美国 Barrett Electric公司研制成功,采用真空管技术自动跟踪钢丝索行走。1954年,由英国人首先去掉了地面上的导引轨道,研制出了采用埋线电磁感应方式跟踪路径的AG。50年代末,AGV开始在欧洲推广使用。1959年,AGV系统在美国开始应用山。由文献-5,12,3]可知:直到70年代,AGv仍然主要采用电磁感应方式引导。AGv的优越性促使其不断发展,应用非常广泛,特别是在工业强国。随着物流系统的完善,AGV系统逐渐与计算机技术相融合,六十年代,计算机技术开始参与AGV系统的控制和管理;1969年,AGV首次在瑞典投入使用,主要集中在制造业12截止到六十年代和七十年代初, Barrett, Webb、 Clark公司占有市场大部分的AGV销售份额;同时在这个时期,AGV导引方式开始发展五花八门,各种环境的适应加速了AGⅤS的迅速发展。八十年代欧洲的AGV技术开始转移到美国,而且随着计算机控制系统的加入,美国使AGv的性能更加先进,AGⅤ控制器可靠性进一步加强,运输量进一步增加;此时,美国的AGV生产商由23家(1983年)增加到74家(1985年),全美国使用AGV的数量增加到3900多台101。1990年,AGⅴ制造强国瑞典NDC开发出新型的基于激光引导的AGV控制系统。同时,AGⅤ在日本的应用也比较成熟。到1988年,日本的AGV制造企业达到20多家,如比较知名的企业大福、 Fanuc公司、村田公司等。日本也成为使用AGV最广泛的国家之一。随着AGV技术的不断提高,美国、日本、欧洲等发达国家的使用已经非常广泛。现在的AGV控制系统装有车载计算机、通讯装置、安全装置和货物装卸设施,自动化程度很高,应用领域相当广泛,汽车制造、造纸、印刷、医药行业是使用量最大的行业,约占全球AGV总销量的80%15。目前,AGV的发展趋势是研究无固定引导路线、高度自由的AGV。122国内发展历史及现状我国第一台AGⅣ在1975年由北京起重运输机械研究所完成,该AGⅤ采用电磁
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