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关键词：: 柴油机电控系统设计电气图纸柴油机电控系统

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柴油机电控系统设计

46页 25000字数+说明书+外文翻译+开题报告+7张CAD图纸【详情如下】

内封.doc

外文翻译--RBF 神经网络.doc

控制系统图.dwg

摘要.doc

柴油机电控系统基本控制方案.dwg

柴油机电控系统基本控制方案.exb

柴油机电控系统设计开题报告.doc

柴油机电控系统设计说明书.doc

球阀和针阀喷油器.dwg

球阀和针阀喷油器.exb

电子控制系统构成框图.exb

目录.doc

系统机构框图.dwg

系统机构框图.exb

组成分析图.dwg

高压共轨系统原理图.dwg

高压共轨系统原理图.exb

高压共轨系统组成分析图.exb

齿条位置传感器等效电路.dwg

齿条位置传感器等效电路.exb

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柴油机电控系统基本控制方案.gif

柴油机电控系统设计.gif

齿条位置传感器等效电路.gif

高压共轨系统原理图.gif

控制系统图.gif

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系统机构框图.gif

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前言1

1.绪论2

2.柴油机电控喷油系统概述7

2.1柴油机电控喷油系统组成7

2.1.1传感器及共他信号输入装置7

2.1.2 ECU8

2.1.3执行器9

2.2柴油机电控制系统控制过程10

2.3柴油机电控系统的控制功能介绍10

3.柴油机电控燃油喷射系统的总体结构分析12

3.1电控系统硬件开发12

3.1.1传感器12

3.1.2 ECU13

4.共轨式燃油喷射系统的工作原理及研究15

4.1共轨燃油喷射系统的工作原理15

4.2电控系统设计方案17

5.电控系统硬件设计19

5.1 ECU硬件结构19

5.2系统的输入及输出信号20

5.2.1转速的测量20

5.2.2油门位置的测量20

5.2.3控制信号的输出21

5.2.4电磁阀开/关检测电路21

6.高压共轨电控液压喷油器的设计22

6.1喷油器的结构、原理和特点22

6.1.1喷油器的工作原理23

6.1.2喷油器的特点24

6.2喷油器喷油率控制25

6.3喷油器关键部件特性的研究26

6.3.1液压控制阀的结构26

6.3.2控制腔的结构参数计算与分析29

6.3.3针阀弹簧的设计34

6.3.4 喷油嘴的结构34

6.3.5 高速电磁阀35

6.4 泄漏与密封分析36

6.5高压共轨喷油器针阀座面应力的计算37

6.6 本章小结37

7经济性分析38

8结论39

致谢40

参考文献41

附录A42

附录B50

摘要

柴油机作为一种动力机在车辆等交通工具及工程机械上有广泛应用，传统机械控制式柴油机结构复杂，控制精度低，由于难以准确及时控制喷油量，喷油提前角等主要参数导致传统机械控制式柴油机在排放性，动力性，经济性等方面弊端越来越突出。国外和我国排放标准要求越来越高，随着电子计算机技术的成熟，柴油机电控技术得到长足发展。本文对柴油机高压共轨式电控喷油系统进行分析研究，对喷油器等关键部件进行较深入研究。对共轨系统工作原理一定了解的基础上，在满足喷油量喷油提前角基本满足的前提下对喷油器进行选型分析。选择球阀喷油器，对其组成部件进行计算分析得到了其最优化结构。

关键词：柴油机；电控系统；共轨式喷射系统；传感器；喷油器

Abstract

diesel engine took one kind of engine on transportation vehicles and the project machinery and so on the vehicles has the widespread application, the traditional machinery control type diesel engine structure is complex, the control precision is low, because the accurate prompt control distributive value, the advance angle of fuel and so on the main parameter causes the traditional machinery control type diesel engine with difficulty in the emissions, the power, aspect malpractices and so on efficiency are more and more prominent. Overseas discharges the standard request with our country to be more and higher, along with the electronic accounting machine technology maturity, the diesel engine electrically controlled technology obtains the considerable development.This article conducts the analysis research to the diesel engine high pressure altogether axle type electrically controlled blow system, and so on the key component carries on to the spray hole studies thoroughly. To the common rail system principle of work certain understanding foundation in, in satisfies under the distributive value advance angle of fuel basic satisfied premise to carry on the shaping analysis to the spray hole. The choice ball valve spray hole, carried on the computation analysis to its composition part to obtain its optimized structure.

Key word: diesel engine; electrically controlled system; altogether axle type injection system; sensor; spray hole与第一代电控燃油喷射系统相比：第二代电控系统采用电磁阀实现对喷射过程的直接数字控制，不但可以控制喷油量，而且可以控制喷射提前角，实现高频和更加灵活的控制功能；可以实现分缸独立控制；能够适应欧Ⅲ及以上排放法规的要求：配合空气控制系统，比如增加增压中冷技术，排放可达欧Ⅲ，再采用可变截面涡轮技术、废气再循环技术、后处理技术等则有些系统可具有欧Ⅳ性能，比如电控单体泵系统。因此，可以有较长的生命周期，是目前市场上急需产品，也是第三代高压共轨系统的技术基础。第二代电控系统的缺点：仍然依赖于传统的脉动高压系统，使得喷射过程仍受到凸轮型线的限制，无法实现大范围的喷射提前角控制；喷射压力的大小只和凸轮型线以及柴油机转速等结构参数有关，不能根据柴油机的工况灵活调节。

　　　第三代电控燃油喷射系统指的是目前国际上比较流行的压力时间控制式的高压共轨电控燃油喷射系统。共轨式电控燃油喷射系统有三种类型：蓄压式电控高压喷射系统、电控液压式泵-喷嘴系统和高压共轨式电控喷射系统。

　　　共轨式电控燃油喷射系统摒弃了传统的脉动供油原理，通过高压共轨、共轨蓄压或液力增压等方式形成高压。高压油泵不直接控制喷油，而是向公共油道供油以维持所需的共轨压力，通过连续调节共轨压力来实现喷射压力的控制。系统采用压力时间式燃油计量原理，靠电磁阀的关闭和开启控制喷射过程。该系统可以实现任何工况、任何转速下的高压喷射，因而对低速低负荷工况特别有利。图 1-3 所示为 Bosch 的高压共轨系统。对于蓄压式和电控液压泵－喷嘴系统来说：控制油压力和增压活塞配合完成喷射压力控制，以较低的共轨力来实现高压喷射；需要活塞增压，增压过程响应慢（可达 30ms 左右）；可以实现预喷射，但是预喷射不能灵活调节；电磁阀采用高电压驱动，实现电磁阀的快速闭合控制。整个控制系统的复杂程度较高。但对于高压共轨系统来说：系统的控制燃油压力可达 180MPa 以上，可以灵活实现预喷射等理想的喷射规律。对喷油量、喷射提前角和喷射压力可以综合的控制。

内容简介：: 中文标题：柴油机电控系统研究外文题目：THE RESEARCH OF DIESEL ENGINE ELECTRICALLY CONTROLLED SYSTEM毕业设计（论文）共58页（其中：外文文献及译文16 页）图纸共7张完成日期2006年6月答辩日期2006年6月附录A2RBF 神经网络2.1神经网络原理人脑存储的信息是分布式地存储在脑细胞之间的关联上，而不是保存在脑细胞的内部。脑细胞通过它们之间的作用关系(如激励和抑制)来存储。人工模拟这种映射关系的系统称为(人工)神经网络(ANN)。神经网络是一个具有高度非线性的超大规模连续时间动力系统，是由大量的处理单元(神经元)广泛互连而形成的网络。它是在现代神经科学研究成果的基础上提出的，反映了脑功能的基本特征。但它并不是人脑的真实描写，而只是它的某种抽象、简化与模拟。网络的信息处理由神经元之间的相互作用来实现，知识与信息的存储表现为网络元件互连间分布式的物理联系，网络的学习和计算决定于各神经元连接权系的动态演化过程。其中，神经元构成了网络的基本运算单元，每个神经元具有自己的阂值，每个神经元的输入信号是所有与其相连的神经元的输出信号和加权后的和。而输出信号是其净输入信号的非线性函数。如果输入信号的加权集合高于其闲值，该神经元便被激活而输出相应的值。在人工神经网络中所存储的是单元之间连接的加权值阵列。神经网络的工作过程主要由两个阶段组成:一个阶段是工作期。此时各连接权值固定，计算单元的状态变化，以求达到稳定状态。另一阶段是学习期(自适应期，或设计期)。此时各计算单元状态不变，各连接权值可修改(通过学习样本或其他方法)。前一阶段较快，各单元的状态亦称短期记忆(S TM)。后一阶段慢的多，权及连接方式亦称长期记(L TM)。目前神经网络的结构有近百种之多，算法更无法记数。根据网络特性，神经网络大致可以分为静态和动态两类。静态网络当前的输出仅仅反映当前输入数据的处理结果。动态网络是有记忆能力的网络，记忆能力可以是由于神经元传递函数是微分或差分方程导致的;也可以是由于网络的输出或网络内部的状态反馈到网络的输入端产生的。下面对于一些常见于控制系统中的网络结构和算法作简要的介绍。 2.1.1神经网络的结构类型1.神经网络的基本结构神经网络是由大量简单神经元相互连接构成的复杂网络。图2-1是一个典型的单层神经网络模型，它具有R维输入，S个神经元。p为RXI维的输入矢量，网络层由权值矩阵W(SxR)、闭值矢量b(Sxl)、求和单元。和传递函数运算单元f组成，S个神经元的输出组成了Sxl维的神经网络输出矢量a。其中，输入层网络权值矩阵W和阀值矢量b的具体形式如下:在单层神经网络的基础上可以构造多层神经网络。一个典型的三层神经网络模型 2.神经网络的分类神经网络的类型多种多样，它们是从不同角度对生物神经系统不同层次的抽象和模拟【18】。一般来说，当神经元的模型确定之后，一个神经网络的特性及其功能主要取决于网络的拓扑结构及学习方法。按网络拓扑结构主要可分为前馈型和反馈型两种19，13。（1）馈型网络。各神经元接受前一层的输入，并输出给下一层，没有反馈，结点分为两类，即输入单元和计算单元，每一计算单元可有多个输入，但只有一个输出(它可藕合到任意多个其他结点作为其输入)。通常前馈网络可分为不同的层，第i层的输入只与第i一1层输出相连，输入和输出结点与外界相连，而其他中间层则称为隐层。（2）馈型网络。所有结点都是计算单元，同时也可接受输入，并向外界输出，可画成一个无向图，如图2-4(a)，其中每个连接弧都是双向的，也可画成如图2-4(b)的形式。若总单元数为n，则每一个结点有n-1个输入和一个输出。神经网络的工作过程主要分为两个阶段:第一个阶段是学习期，此时各计算单元状态不变，各连接上的权值可通过学习来修改;第二阶段是工作期，此时各连接权固定，计算单元状态变化，以达到某种稳定状从作用效果看，前馈网络主要是函数映射，可用于模式识别和函数逼近。反馈网络按对能量函数的极小点的利用来分类有两种:第一类是能量函数的所有极小点都起作用，这一类主要用作各种联想存储器;第二类只利用全局极小点，它主要用于求解最优化问题。2.1.2神经网络在控制中的应用 K.J.Hunt和D.Sbrabaro等总结了神经网络用于控制系统最吸引人的几个特征:神经网络是本质的非线性系统、具有高度并行的结构、某些网络可以硬件实现、具有学习和自适应性、可以同时处理定性的和定量的数据、多变量系统等特点。神经网络在控制系统中无论是作为控制器还是作为实际系统的辩识模型，都是以神经网络的函数逼近能力为基础的。有两种基本的神经网络的应用方式:正模型(辩识)和逆模型(控制器) 正模型法通过训练使一个神经网络逼近一个系统的正向模型，以模型和实际系统输出的差值作为网络训练的误差信号来修正网络权值。假设系统的模型训练可以一直采用实际系统的数据y以保证训练的稳定性，或者在一定的训练步数后采用神经网络以前的输出作为网络输入来避免有噪声的数据对辩识结果的影响。逆模型法通过训练得到系统动态的逆模型或控制器，串联在原系统前面使系统简化或满足一定的控制要求。有两种基本的神经网络求逆结构:直接逆结构和间接逆结构直接逆结构输入一个信号到实际系统，其输出作为神经网络的输入，把网络的输出和加入信号的差值作为误差训练网络。直接逆结构有两个严重的缺点: (l)由于神经网络逆模型的输入是由实际系统的输出得到的，可能不完全覆盖逆问题的输入空间; (2)如果系统不是一对一的，在不能得到全部输入空间特征训练点集的情况下，可能得到错误的逆模型。采用神经网络间接逆结构可以部分地克服上面两点缺点。间接逆结构输入信号到神经网络，然后把网络输出送到实际系统，系统输出与输入到网络的信号差值用来训练神经网络。采用该结构，网络的输入可以人工选择，可以使输入的信号能够代表逆系统输入空间的特征。当网络不是一对一的时候，可以学习得到具有某些特定性质的部分逆模型。2.2径向基函数神经网络及其学习算法径向基函数(Radial Basis Function)神经网络是由J.Moody和C.Darken在20世纪80年代末提出的一种神经网络，它是具有单隐层的三层前馈网络。由于它模拟了人脑中局部调整、相互覆盖接收域(或称感受野一RecePtive Field)的神经网络结构，因此，径向基函数(径向基)神经网络网络是一种局部逼近网络，即对于输入空间的某一局部区域只存在少数的神经元用于决定网络的输出。已证明它能以任意精度逼近任意连续函数。径向基函数理论是一种对多输入、多输出非线性系统的辨识方法，以此而建立的径向基网络可实现对非线性系统的模式识别与分类。2.2.1径向基函数神经元模型一个具有R维输入的径向基函数神经元模型如图2一7所示。图中的dist模块表示求取输入矢量和权值矢量的距离。此模型中采用高斯函数radbas作为径向基函数神经元的传递函数，其输n入为输入矢量p和权值矢量w的距离乘以阂值b。高斯函数是典型的径向基函数，其表达式为f(x)=e-x2其函数曲线如图2一8所示中心与宽度是径向基函数神经元的两个重要参数。神经元的权值矢量w确定了径向基函数的中心，当输入矢量p与w重合时，径向基函数神经元的输出达到最大值，当输入矢量p距离w越远时，神经元输出就越小。神经元的闭值b确定了径向基函数的宽度，当b越大，则输入矢量p在远离w时函数的衰减幅度就越大。2.2.2径向基函数神经网络模型一个典型的径向基函数网络包括两层，即隐层和输出层。图2-9是一径向基函数网络的结构图。图中所示网络的输入维数为R、隐层神经元个数为S1、输出个数S2，隐层神经元采用高斯函数作为传递函数，输出层的传递函数为线性函数。3.模糊控制3.1模糊控制的基本思想 PID控制在生产过程中是一种最普遍采用的控制方法，在船舶自动控制中获得广泛应用。但其在局限性使得采用PID控制难以获得满意的控制结果。模糊控制器是一种近年来发展起来的新型控制器，其优点是不要求掌握受控.对象的精确模型。模糊控制具有模糊控制灵活而适应性强的优点，可达到较好的控制效果。模糊控制的基本思想是利用计算机来实现人的控制经验，而这些经验多是用语言表达的具有相当模糊性的控制规则。模糊控制器(F uzzy Controller，即FC)获得巨大成功的主要原因在于它具有如下一些突出特点:模糊控制是一种基于规则的控制。它直接采用语言型控制规则，出发点是现场操作人的控制经验或相关专家的知识，在设计中不需要建立被控对象的精确数学模型，因而使得控制机理和策略易于接受与理解，设计简单，便于应用。由工业过程的定性认识出发，比较容易建立语言控制规则，因而模糊控制对那些数学模型难以获取、动态特性不易掌握或变化非常显著的对象非常适用。基于模型的控制算法及系统设计方法，由于出发点和性能指标的不同，容易导致较大差异;但一个系统的语言控制规则却具有相对的独立性，利用这些控制规律间的模糊连接，容易找到折中的选择，使控制效果优于常规控制器。模糊控制算法是基于启发性的知识及语言决策规则设计的，这有利于模拟人工控制的过程和方法，增强控制系统的适应能力，使之具有一定的智能水平。大减弱，尤其适合于非线性、时变及纯滞后系统的控制。糊逻辑控制器的结构与设计实践证明，与传统的PID调节器相比，模糊控制器有更快的响应和更小的超调，对过程参数的变化不很敏感，所有点都能得到控制。在一些难以建立数学模型的过程或具有大纯滞后的过程控制中，模糊控制器取得了明显效果。对于控制对象具有大纯滞后的特性等难以控制的对象，事先也无法知道具体对象有数学模型，采用模糊控制算法有较好的控制效果。3.2模糊控制器及其基本结构模糊控制系统一般按输入误差和误差变化对过程进行控制。模糊控制器是一种语言控制器，采用模糊集理论实现对过程的控制，其基本思路是模拟操作人员凭经验积累起来的控制策略，对一些难以构造数学模型的过程进行控制。模糊控制器的基本结构包括输入输出变量，模糊化处理部分，控制算法部分，模糊判决部分。根据模糊控制器的输入输出变量不同可以将模糊控制器分为以下几种形式:(1)单输入单输出模糊控制器;(2)二维输入单输出模糊控制器;(3)三维输入单输出模糊控制器;(4)多维输入输出模糊控制器。其中由于二维输入单输出模糊控制器一般选用偏差和偏差变化能够较好的反映被控对象的动态特性，控制效果好目前得到广泛的应用。其中yr为系统的设定值，y为系统输出，“e和ec分别是系统偏差和偏差的微分信号，也就是模糊控制器的输入，。为控制器输出的控制信号，E，Ec，U为相应的模糊量。由图可知模糊控制器主要包含三个功能环节:用于输入信号处理的模糊量化和模糊化环节，模糊控制算法功能单元，以及用于输出解模糊化的模糊判决环节。模糊控制器设计的基本方法和主要步骤大致包括:首先将实际测得的精确误差的变化e和误差变化率ec成经过模糊处理变换成模糊量，在采样时刻k，误差变化的定义为上式中yr和yk分别表示设定值和k时刻的过程输出，ek即为k时刻的输出误差。用这些量来计算模糊控制规则，然后又变成精确量对过程进行。检测输入变化量e和的值进行标尺变换，将输入变量值变换成相应的论域;将输入数据转换成合适的语言值，这一部分是由模糊量化和模糊化环节完成:1.选定模糊控制器的输入输出变量，并进行量程转换。选取方法一般依据具体控制过程的参数决定。2.确定各变量的模糊语言取值及相应的隶属函数，即进行模糊化。模糊语言值通常选取3、5或7个，例如取为负，零，正，负大，负小，零，正小，正大，或负大，负中，负小，零，正小，正中，正大等。然后对所选取的模糊集定义其隶属函数，模糊集的隶属函数应该根据实际情况来确定。一般情况通常采用下式来拟合模糊集合的隶属度:但是在实际应用时三角形隶属函数或高斯型分布曲线也保持较高的精度，其灵活性也比较大，而且可以大大的减少模糊化的计算工作量，方便程序设计。其分布可以依据问题的不同取为均匀间隔或非均匀的;也可采用单点模糊集方法进行模糊化。下图为一个三角形隶属函数取法示意图:在确定模糊子集的隶属函数时需要注意如下问题:第一隶属函数的偏差采用模糊集合的宽度越窄，则灵敏度，精度越高;隶属函数的偏差采用模糊集合的宽度越宽，则分辨率较低，偏差控制灵敏度也相应较低，控制特性较平缓，稳定性也较好，因此，一般在误差较大时采用低分辨率的隶属函数;误差较小时，宜采用高分辨率的隶属函数。第二定义变量的全部模糊集合时，如PL，NL，应考虑它们对论域一n，n的覆盖程度，使论域中的任何一点位于这些模糊集合里:隶属度的最大值都不能太小，否则有可能在这些点上会出现“空挡”，引起失控。因此全部模糊集合所包含的与非零隶属度对应的论域元素个数应当是模糊集合总数的3-4倍。第三考虑各个隶属集合之间的相互影响，可以采用这些模糊集合中的任意两个集合交集中的最大值B来衡量。B小时，反应控制灵敏度高;B大时鲁棒性好，即模糊控制器对于被控对象的参数变化适应性强。一般取B=0.4一0.8，B值不宜过大，否则对两个模糊集合很难区分。3.3模糊控制器知识库及推理建立模糊控制规则或控制算法。这是指规则的归纳和规则库的建立，是从实际控制经验过渡到模糊控制器的中心环节。3.3.1.模糊控制数据库模糊控制的输入变量，输出变量经模糊化处理后，其全部模糊子集的隶属度或隶属函数存放于模糊控制器的数据库中，在规则推理的模糊关系方程求解过程中，为推理机提供数据。3.3.2.模糊控制器规则库模糊控制器规则是基于专家只是或操作者长期积累的经验，是模仿人的直觉推理的一种语言形式。模糊控制规则通常表述为一组IF-THEN结构的模糊条件语句构成，例如:IF e=N ANDec=N，THEN u=PB.等表达形式;其相应的语言变量分别为E，Ec和U。如果将所有规则逐条列出是比较繁琐，为简明起见，通常将所有模糊控制规则总结为模糊控制规则表，如表1中所示，可直接由E和Ec查询相应的控制量U。规则库是用于存放全部模糊控制规则，为模糊推理提供规则，模糊控制规则的多少与语言变量模糊子集的划分有关，这种划分越细，规则就越多，但并不意味这规则库的准确度越高，规则库的准确度越高。规则库的准确度还是依赖于专家知识的准确度。规则库和数据库共同组成了:控制器的知识库。3.3.3.模糊控制器推理模糊控制器推理是根据输入变量，由模糊控制规则进行模糊推理，求解出模糊关系方程，并获取模糊控制量的过程。模糊推理有时也称似然推理。其一般形式如下:(l)一维控制器推理前提:IF A=A1，THEN B=B1;条件:IF A=A2，结论:THEN B=?(2)二维控制器推理前提:IF A=A1 AND B=B1，THENC=c1;条件:IF A=A2 AND B=B2，结论:THENC=?当上述给定条件为模糊集时，可以采用似然推理。在模糊控制中，由于控制器的输入变量(如偏差和偏差变化率)往往不是一个模糊子集，而是一些孤点(如a=a0，b=b。)等。因此这种推理方式一般不直接使用，模糊推理方式一般分为以下三类推理方式:Mamdani法;拉森乘积运算法和日本学者TSukamoto法。下面介绍一种较为广泛引用的Mamdani模糊推理法:Mamdani法又称极小运算法。设a=a。，b=b。，则新的隶属度为:该方法常用于模糊控制系统中，直接采用极大极小合成运算方法，计算较为简便，在模糊控制器的设计运用中得到大量使用。3.4模糊控制量的反模糊化处理由于被控对象每次只能接受一个精确控制量，无法接受模糊控制量，因此必须从模糊量中提取一个精确的控制量，这一过程即为模糊量的反模糊化处理，又称模糊判决。通常有如下几种方法:最大隶属度法，加权平均法和取中位数法。进行反模糊化时，若采用最大隶属度法，结果精确但软件实现较困难;采用最大值法，虽结构简单，但结果不精确。加权平均法是其中应用较为广泛的一种判决方法，兼顾了二者的优点。所以选用加权平均法，兼顾了二者的优点。执行量由下式决定:式中，ci分别为各元素Ui在集合中和加权系数。3.5模糊控制器基本设计原则和途径模糊控制器是一种利用人的直觉和经验设计的控制专家系统，设计时不能用数学模型来描述受控系统的特性，目前还没有一个固定的设计过程和方法。尽管如此，我们仍然可以总结出原则性设计步骤。3.5.1模糊控制器设计步骤(l)定义输入输出变量根据受控系统所要求的检测状态和操作控制作用分别确定模糊控制器的输入变量和输出变量。(2)定义所有变量的模糊化条件根据受控系统实际情况，确定输入变量的测量范围和输出变量的控制作用范围然后确定每个变量的论域，根据变量论域安排各个变量的语言术语及其对应的隶属函数。(3)设计控制规则库根据专家知识和熟练操作者的操作经验将受控系统的控制过程用语言表述出来，经整理后建成系统控制规则库。(4)设计模糊推理结构根据设计的需要，可以使用软件在通用计算机上来实现选择模糊判决的方法模糊控制器的输出是一个模糊量，为了实现对外设备的控制，必须选择合适的模糊判决方法，将控制输出的模糊量转换为精确量。3.5.2模糊控制器设计途径目前设计模糊控制器的途径一般是从三个方面考虑(1)根据专家知识和经验进行设计:模糊控制器本身就是本身是应用于控制的专家系统，其设计根据就是专家的经验和知识。(2)根据建立熟练操作者控制模型的方法进行设计:控制专家和熟练操作者可以巧妙地根据其经验实现对复杂系统的控制，但是这种方法难以把他们的经验和诀窍用逻辑形式表达出来。(3)根据建立被控对象模糊控制模糊模型的方法进行设计通过建立控制对象的模糊模型来实现，这种方法主要适用于无法依据该领域有经验的专家的经验，这时只有通过设计方法来建立相应的模糊规则。附录B2. RBF neural network 2.1 neural network principleThe human brain saves the information is distributional saves between the brain cells in the connection, but is not the preservation in brain cell in. The brain cell the function relations (for example drive and suppression) save through them between. The man-power simulates this kind of mapping relations the system to be called (artificially) neural network (ANN). The neural network is one has the highly non-linear ultra large-scale run-on time dynamic system, is network which (neuron) the widespread interconnection forms by the massive processing unit. It is proposes in the modern neuroscience research results foundation, has reflected the brain function basic characteristic. But it is not the human brain real description, but only is its some kind abstract, the simplification and the simulation.The network information processing does mutually by the neuron between uses for to realize, the knowledge and the information memory performance for the circuit component interconnection the distributional physical relation, the network study and the computation decides in various neurons connection power departments dynamic evolutionary process. Among them, the neuron constituted the network fundamental operation unit, each neuron has own cuts off from the value, after if each neuron input signal is all the connected neuron output signal and the weighting sum. But the output signal is its net input signal nonlinear function. If the input signal weighting set is higher than its idle value, this neuron is then activated outputs the corresponding value. Is between the unit saves which in the artificial neural networks connects the weighted value array.The neural network work process mainly is composed by two stages:A stage is the work time. This time each connection weight is fixed, the computing element change of state, achieves the steady state in order to. Another stage is studies the time (auto-adapted time, or design time). This time each computing element condition is invariable, each connection weight may revise (through study sample or alternative means). The preceding stage is quick, various units condition also calls the short-term memory (S TM). Latter stage slow many, the power and the connection way also called records (L TM) for a long time.At present the neural network structure has near hundred kind of many, the algorithm is unable to register. According to the network characteristic, the neural network may divide into the static state and the dynamic two kinds approximately. Static network current output merely reflection current data-in processing result. The dynamic network has the memory ability network, memory ability may be because the neuron transfer function is the differential or the difference equation causes;Also may be because the network output or the network internal condition feed back to the network input end produce. Below regarding some common makes the brief introduction in the control system network architecture and the algorithm.2.1.1 Neural network structure type1. Neural network basic structureThe neural network is connects the constitution mutually by the massive simple neurons the complex network. Figure 2-1 is a typical monolayer neural network model, it has the R Uygur to input, S neuron. p is the RXI Uygurs input vector, the network level by weight matrix W(SxR), shuts value vector b(Sxl), the summation unit. With the transfer function arithmetical unit f composition, S neuron output has composed Sxl Uygurs neural network output vector a. Among them, input level network weight matrix W and the valve value vector b concrete form is as follows:In the monolayer neural network foundation may the structure multi-layer neural network. A typical three neural network model2. Neural network classificationNeural network type many and varied, they are from the different angle to the biology nervous system different level abstract and the simulation 18. Generally speaking, after neuron model determination, a neural network characteristic and the function mainly are decided by network topology and the study method. Mainly may divide into the forward feed according to the network topology and the feedback two kinds “19,13”.(1)Feeds the network. Various neurons accept the preceding input, and outputs for next, does not have the feedback, the point to divide into two kinds, namely the input unit and the computing element, each computing element may have many inputs, but only then an output (it may take its input willfully pale pinkish purple to many other points). The usual forward feed network may divide into the different level, the ith input is only connected with a ith 1 output, the input and the output point and the outside are connected, but other intermediate levels are called the implicit strata. (2)Feeds the network. All points all are the computing elements, simultaneously also may accept the input, and to the outside output, may draw becomes one not to have to the chart, like Figure 2-4(a), in which each connection arc all is bidirectional, also may draw becomes like Figure 2-4(b) form. If the cargo certificate number is n, then each point has a n-1 input and an output.The neural network work process mainly divides into two stages:The first stage studies the time, this time each computing element condition is invariable, connects respectively the weight may revise through the study;The second stage is the work time, this time each connection power is fixed, the computing element change of state, achieves some kind of stable shapeLooking from the function effect that, the forward feed network mainly is the function mapping, available in pattern recognition and approximation of function. The feedback network according to classifies to the energy function minimum point use has two kinds:The first kind is energy function all minimum points all has an effect, this kind mainly serves as each kind of association memory;The second kind only uses the overall situation minimum point, it mainly uses in solving the optimization problems.2.1.2 Neural networks in control applicationK.J.Hunt and D.Sbrabaro and so on summarized the neural network to use in the control system most appealing several characteristics:The neural network is the essential nonlinear system, has the highly parallel structure, certain networks may the hardware realization, have the study and auto-adapted, may the simultaneous working qualitative and characteristics and so on quota data, many-variable system. Neural network in control system regardless of is as the controller or took the actual system debates knows the model, all is take neural network approximation of function ability as the foundation. Some two basic neural network application way:The model (debates knows) and the counter model (controller)The modeling enable a neural network through the training to approach a system the forward model, takes the network training by the model and the actual system output interpolation the error signal corrective net weight. The supposition system model training may always use the actual system data y to guarantee the training the stability, or uses the neural network beforehand output after certain training step to avoid as the network input having the noise data to debate knows the result the influence.The counter modeling obtains the system dynamic counter model or the controller through the training, connects causes the system in front of the original system to simplify or to satisfy certain control request. Some two kind of basic neural network asks the counter structure:The direct counter structure and the indirect counter structure direct counter structure inputs a signal to the actual system, its output took the neural network the input, and joins the network output the signal the interpolation to train as the error the network. The direct counter structure has two serious shortcomings:(l) The neural network counter model input is obtains by the actual system output, possibly incompletely covers the inverse problem the input space;(2) If the system is not one pair one, in cannot obtain in the completely input space characteristic training set of points situation, possibly obtains the wrong counter model. Uses the neural network indirect counter structure to be possible to overcome above two shortcomings partially.The indirect counter structure input signal to the neural network, then delivers the network output the actual system, the system outputs and inputs uses for to the network signal interpolation to train the neural network. Uses this structure, the network input may the artificial selection, may enable the input the signal to represent the counter system input space the characteristic. When the network is not a pair of time, may study obtains has certain specific nature part counter model.2.2 radial direction primary function neural network and study algorithmThe radial direction primary function (Radial Basis Function) the neural network is by J.Moody and C.Darken one kind of neural network which proposed in the end of 1980s, it has the single implicit strata three forward feed network. Because it simulated in the human brain the partial adjustment, has mutually covered the receive territory (or called receptive field RecePtive Field) the neural network structure, therefore, the radial direction primary function (radial direction base) the neural network network was one kind partial approaches the network, namely only had the minority neuron regarding input space some partial region to use in deciding the network the output. Had proven it can approach willfully the continuous function by the random precision. The radial direction primary function theory is one kind to the multi-inputs, the multi-output nonlinear system identification method, establishes the radial direction base network by this to be possible to realize to the nonlinear system pattern recognition and the classification.2.2.1 Radial direction primary function neuron modelThe radial direction primary function neuron model like chart which has the R Uygur to input 2 7 shows. In chart dist module expression seeking input vector sum weight vector distance. In this model uses gaussian function radbas to take the radial direction primary function neuron the transfer function, it loses n to enter is multiplied by for input vector p and the weight vector w distance cuts off from value b. The gaussian function is the typical radial direction primary function, its expression is f(x)=e-x2 its function curve like chart 2 one 8 shows the center and the width is the radial direction primary function neuron two important parameters. Neuron weight vector w has determined the radial direction primary function center,When input vector p and w superposition, radial direction primary function neuron output maximizing, when input vector p is away from w is farther, the neuron outputs is smaller. The neuron shut value b to determine the radial direction primary function width, when b was bigger, then input vector p when was far away w the function weaken scope was bigger. 2.2.2 Radial direction primary function neural network modelA typical radial direction primary function network including two, namely implicit strata and output level. Figure 2-9 is a radial direction primary function network structure drawing. In the chart shows the network the input dimension is R, the implicit strata neuron integer is S1, output integer S2, the implicit strata neuron uses the gaussian function to take the transfer function, the output level transfer function for the linear function.3. Controls 3.1 fuzzy controls fuzzily the basic thoughtThe PID control in the production process is the control method which one kind uses most generally, obtains the widespread application in the ships automatic control. But it causes in the limitation to use the PID control to obtain satisfaction with difficulty the control result. The fuzzy controller is the new controller which one kind of recent years developed, its merit did not request the grasping controlled. Object precise model. But the fuzzy control has the fuzzy control nimble the compatible strong merit, may achieve the good control effect. The fuzzy control basic thought is realizes humans control experience using the computer, but these experience many has the suitable fuzziness control rule with the language expression. The fuzzy controller (F fuzzy Controller, namely FC) obtains the huge success the primary cause to lie in it to have the following some prominent characteristicsThe fuzzy control is one kind based on the rule control. It uses the language control rule, the starting point is directly the scene operates humans control experience or the correlation experts knowledge, does not need to establish the accusation object in the design the precise mathematical model, thus causes the system mechanism and the strategy easy to accept with the understanding, the design is simple, is advantageous for the application. Embarks by the commercial run qualitative understanding, compared with easy to establish the language control rule, thus controls fuzzily to these mathematical model gains, the dynamic characteristic with difficulty not easy to grasp or the change extremely remarkable object is suitable extremely. Based on the model control algorithm and the system design method, as a result of the starting point and the performance index difference, easy to cause the big difference;But a system language control rule has the relative independence actually,Using these control rule fuzzy connection, easy to find the compromise the choice, causes the control effect to surpass the conventional controller. The fuzzy control algorithm is based on the inspirational knowledge and the language decision-making rule design, this is advantageous to the simulation manual control process and the method, enhancement control system adaptiveness, enable it to have certain intelligent level. Weakens greatly, suits especially in the non-linearity, time-variable and the pure time delay system control. Sticks the logical controller the structure and the design practice proof, compares with the traditional PID regulator, the fuzzy controller has a quicker response and a smaller over modulation, to the process parameter change not very sensitive, all spots all can be under the control. Establishes the mathematical model with difficulty in some the process or has in the big pure time delay process control, the fuzzy controller has obtained the tangible effect. Has the object regarding the controlled member which the big pure time delay characteristic and so on controls with difficulty, also is unable to know beforehand the concrete object has the mathematical model, uses the fuzzy control algorithm to have the good control effect.3.2 fuzzy controllers and basic structureThe fuzzy control system generally carries on the control according to the loading error and the erroneous change to the process. The fuzzy controller is one language controller, uses the fuzzy set theory realization to the process the control, its basic mentality is simulates the operator to depend on the control strategy which the experience accumulates, carries on the control with difficulty to some structure mathematical model process.The fuzzy controller basic structure including the input output variable, the fuzzy processing part, the control algorithm part, decides the part fuzzily. Different may blur according to the fuzzy controller input output variable the controller to divide into following several forms:(1) single input list output fuzzy controller;(2) two-dimensional input list output fuzzy controller;(3) three dimensional input list output fuzzy controller;(4) multi-dimensional input output fuzzy controller. Because in which the two-dimensional input list output fuzzy controller selects the deviation and the deviation change generally can the good reflection accusation object dynamic characteristic, control effect good at present obtains the widespread application.Yr is the system setting value, y is the system output, “e and ec respectively are the system deviation and the deviation differential signal, also is the fuzzy controller input. The control signal outputs which for the controller, E, Ec, U is the corresponding fuzzy quantity. The figure shows the fuzzy controller mainly contains three function links:Uses in input signal processing the fuzzy quantification and the fuzzy link, controls the algorithm function unit fuzzily, as well as uses in outputting the solution fuzzy the fuzzy decision link. The fuzzy controller designs the essential method and the main step include approximately:First actually will obtain precise erroneous change e and erroneous rate of change ec will become passes through fuzzy processing to transform the fuzzy quantity, in sampling time k, erroneous change definition forIn the above equation yr and yk expressed separately the setting value and the k time process output, ek is the k time outlet error. Calculates the fuzzy control rule with these quantities, then turns the precise quantity to carry on to the process. The examination input change measures e and the value carries on the rod to transform, will input the variable value to transform the corresponding universe of discourse; The data-in will transform the appropriate linguistic value, this part will be completes by the fuzzy quantification and the fuzzy link:1. Designated the fuzzy controller the input output variable, and carries on the measuring range transformation. Selection method general basis concrete controlled process parameter decision. 2. Determined various variables the fuzzy language value and the corresponding membership function, namely carry on the fuzzy. The fuzzy linguistic value usually selects 3, 5 or 7, for example takes for negative, zero, negative big, negative small, zero, just small, honorable, or negative big, negative, negative small, zero, just small, center, honorable and so on. Then to the fuzzy set which selects defines its membership function, the fuzzy set membership function should act according to the actual situation to determine. The ordinary circumstances usually use the equation below to fit the fuzzy set the degree of membership:But when practical application the triangle membership function or the Gaussian distribution curve also maintain the high precision, its flexibility quite is also big, moreover may the big reduced fuzzy computation work load, the convenient programming. Its distribution may take differently based on the question for the even gap or inhomogeneous; Also may use the simple point fuzzy set method to carry on the fuzzy. The next chart is a triangle membership function access method schematic drawing:When determination fuzzy subset membership function needs to pay attention to the following question:The first membership function deviation uses the fuzzy set the width to be narrower, then the sensitivity, the precision is higher; The membership function deviation uses the fuzzy set the width to be wider, then the resolution is low, the deviation controls the sensitivity correspondingly also low, the control characteristic is gentle, the stability is also good, therefore, is generally big when the error uses the low resolution the membership function; When the error is small, uses the high resolution suitably the membership function.The second definition variable complete fuzzy set is fashionable, if PL, NL, should consider they to universe of discourse n, n” the cover degree, causes in the universe of discourse any to be located in these fuzzy sets: The degree of membership maximum value all cannot too be small, otherwise has the possibility to light in these can appear “the neutral”, causes loses control. Therefore the fuzzy set contains completely and the non-vanishing degree of membership correspondence universe of discourse element integer must be fuzzy set total 3-4 time. Third considered between each subordination set the mutual influence, may use in these fuzzy sets in random two set occurring together maximum value B to weigh. B hour, the response control sensitivity is high; The B big time robustness is good, namely fuzzy controller regarding accusation object parameter change compatible. Takes B=0.4YI0.8, B the value generally not not suitably oversized, otherwise is very difficult to two fuzzy sets to differentiate.3.3 fuzzy controller knowledge libraries and inferenceEstablishment fuzzy control rule or control algorithm. This is refers to the rule the induction and the regular storehouse establishment, is after examines from the actual control transits to the fuzzy controller center link. 3.3.1.The fuzzy control databaseFuzzy control input variable, the output variable after fuzzy processing, it completely fuzzy subset degree of membership or subordination letter number depositing in the fuzzy controller database, in the rule inference fuzzy relationship equation solution process, provides the data for the inducing equipment.3.3.2.Fuzzy controller regular storehouseThe fuzzy controller rule is based on the expert is only the OR operation long-term accumulation experience, imitates humans intuition inference one language form. The fuzzy control rule usual indication is a group of IF-THEN structure fuzzy statement constitution, for example: IF e=N ANDec=N, THEN u=PB.Expression forms;Its corresponding language variable respectively is E, Ec and U. If will possess the rule one by one to list will be quite tedious, for simplicity, will usually possess the fuzzy control rule to summarize for the fuzzy control rule table, if in table 1 will show, might directly inquires the corresponding control by E and Ec to measure U. The regular storehouse is uses in depositing completely the fuzzy control rule, provides the rule for the fuzzy reasoning, controls the rule fuzzily how many with the language variable fuzzy subset division related, this kind divides thin, the rule are more, but did not mean this regular storehouse the accuracy is higher, the regular storehouse accuracy is higher. The regular storehouse accuracy relies on the expert knowledge accuracy. The regular storehouse and the database have composed together: Controller knowledge library.3.3.3. The fuzzy controller inference fuzzy controller inference is according to the input variable, carries on the fuzzy reasoning by the fuzzy control rule, solves the fuzzy relationship side regulation, and gains the fuzzy control quantity process. Sometimes the fuzzy reasoning also calls the likelihood inference. Its general form is as follows:(l) one-dimensional controller inference premise: IF A=A1, THEN B=B1;Condition:IF A=A2, conclusion: THEN B=?(2) two-dimensional controller inference premise: IF A=A1 AND B=B1, THENC=c1;Condition:IF A=A2 AND B=B2, conclusion:THENC=?When above assigns the condition for the fuzzy set, may use the likelihood inference. In fuzzy control, because the controller input variable (for example deviation and the deviation rate of change) is often not a fuzzy subset, but is some orphaned spots (for example a=a0, b=b.)And so on. Therefore this inference way not directly uses generally, the fuzzy reasoning way divides into following three kind of inference way generally:Mamdani law;Lasson product operational method and Japanese scholar TSukamoto law. Following introduces one kind of more widespread quotation the Mamdani fuzzy line or method of reasoning:The Mamdani law calls the minimum operational method. Supposes a=a. b=b. Then the new degree of membership is:This method commonly used in the fuzzy control system, directly uses the maximum and minimum synthesis operational method, the computation is simple, obtains the massive uses in the fuzzy controller design utilization.3.4 fuzzy control quantity counter-fuzzyProcessing the accusation object each time only can accept an accuracy control quantity, is unable to accept the fuzzy control quantity, therefore must withdraw a precise control quantity from the fuzzy quantity, this process namely for fuzzy quantity counter-fuzzy

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