外文资料与翻译
PID Contro l
6.1 Introduction
The PID controller is the most common form of feedback. It was an essential element of early governors and it became the standard tool when process control emerged in the 1940s. In process control today , more than 95% of the control loops are of PID type, most loops are actually PI control. PID controllers are today found in all areas where control is used. The controllers come in many different forms. There are standalone systems in boxes for one or a few loops, which are manufactured by the hundred thousands yearly. PID control is an important ingredient of a distributed control system. The controllers are also embedded in many special purpose control systems. PID control is often combined with logic, sequential functions, selectors, and simple function blocks to build the complicated automation systems used for energy production, transportation, and manufacturing. Many sophisticated control strategies, such as model predictive control, are also organized hierarchically. PID control is used at the lowest level; the multivariable controller gives the set points to the controllers at the lower level. The PID controller can thus be said to be the “bread and butter of control engineering. It is an important component in every control engineer’s tool box.
PID controllers have survived many changes in technology , from mechanics and pneumatics to microprocessors via electronic tubes, transistors, integrated circuits. The microprocessor has had a dramatic influence the PID controller. Practically all PID controllers made today are based on microprocessors. This has given opportunities to provide additional features like automatic tuning, gain scheduling, and continuous adaptation.
6.2 Algorithm
We will start by summarizing the key features of the PID controller. The “textbook” version of the PID algorithm is described by:
()()()()???
?
?
?++=?dt t de d e t e K t u T T d
t
i 01
ττ 6.1
where y is the measured process variable, r the reference variable, u is the control signal and e is the control error （e =sp y ? y ）. The reference variable is often called
the set point. The control signal is thus a sum of three terms: the P-term （which is proportional to the error）, the I-term （which is proportional to the integral of the error）, and the D-term （which is proportional to the derivative of the error）. The controller parameters are proportional gain K, integral time T i, and derivative time T d. The integral, proportional and derivative part can be interpreted as control actions based on the past, the present and the future as is illustrated in Figure 2.2. The derivative part can also be interpreted as prediction by linear extrapolation as is illustrated in Figure 2.2. The action of the different terms can be illustrated by the following figures which show the response to step changes in the reference value in a typical case.
Effects of Proportional, Integral and Derivative Action
Proportional control is illustrated in Figure 6.1. The controller is given by D6.1E with T i= and T d=0. The figure shows that there is always a steady state error in proportional control. The error will decrease with increasing gain, but the tendency towards oscillation will also increase.
Figure 6.2 illustrates the effects of adding integral. It follows from D6.1E that the strength of integral action increases with decreasing integral time T i. The figure shows that the steady state error disappears when integral action is used. Compare with the discussion of the “magic of integral action” in Section 2.2. The tendency for oscillation also increases with decreasing T i. The properties of derivative action are illustrated in Figure 6.3.
Figure 6.3 illustrates the effects of adding derivative action. The parameters K and T i are chosen so that the closed loop system is oscillatory. Damping increases with increasing derivative time, but decreases again when derivative time becomes too large. Recall that derivative action can be interpreted as providing prediction by linear extrapolation over the time T d. Using this interpretation it is easy to understand that derivative action does not help if the prediction time T d is too large. In Figure 6.3 the period of oscillation is about 6 s for the system without derivative Chapter 6. PID Control
Figure 6.1
Figure 6.2
Derivative actions cease to be effective when T d is larger than a 1 s (one sixth of the period). Also notice that the period of oscillation increases when derivative time is increased.
A Perspective
There is much more to PID than is revealed by （6.1）. A faithful implementation of the equation will actually not result in a good controller. To obtain a good PID controller it is also necessary to consider。
Figure 6.3
? Noise filtering and high frequency roll off ? Set point weighting and 2 DOF ? Windup ? Tuning
? Computer implementation
In the case of the PID controller these issues emerged organically as the technology developed but they are actually important in the implementation of all controllers. Many of these questions are closely related to fundamental properties of feedback, some of them have been discussed earlier in the book.
6.3 Filtering and Set Point Weighting
Differentiation is always sensitive to noise. This is clearly seen from the transfer function G (s ) =s of a differentiator which goes to infinity for large s . The following example is also illuminating.
()()t t t n t t y n
n
a
ω
sin
sin sin +
=+=
where the noise is sinusoidal noise with frequency w. The derivative of the signal is
()()t
t t n t dt
t dy n n
a
ωcos cos cos +
=+=
The signal to noise ratio for the original signal is 1/a n but the signal to noise ratio of the differentiated signal is w/a n . This ratio can be arbitrarily high if w is large.
In a practical controller with derivative action it is there for necessary to limit the high frequency gain of the derivative term. This can be done by implementing the derivative term as
N
s s
D T
KT d
d +-
=1 6.2
instead of D =sT d Y . The approximation given by (6.2) can be interpreted as the ideal derivative sT d filtered by a first-order system with the time constant T d /N . The approximation acts as a derivative for low-frequency signal components. The gain, however, is limited to KN . This means that high-frequency measurement noise is amplified at most by a factor KN . Typical values of N are 8 to 20.
Further limitation of the high-frequency gain
The transfer function from measurement y to controller output u of a PID controller with the approximate derivative is
()???
?
?
?++
+
-=N s s S K S C T
KT T d
d
I
11
1 This controller has constant gain
()()N s C K s +-=∞
→1lim
at high frequencies. It follows from the discussion on robustness against process variations in Section 5.5 that it is highly desirable to roll off the controller gain at high frequencies. This can be achieved by additional
low pass filtering of the control signal by
()()
T s s F f n
+=
11
where T f is the filter time constant and n is the order of the filter. The choice of T f is a compromise between filtering capacity and performance. The value of T f can be coupled to the controller time constants in the same way as for the derivative filter above. If the derivative time is used, T f = T d /N is a suitable choice. If the controller is only PI, T f =Ti /N may be suitable.
The controller can also be implemented as
()()
N T s T T d
s s K s C d i
+???
? ?
?++-=12
1
1
1 6.3
This structure has the advantage that we can develop the design methods for an ideal PID controller and use an iterative design procedure. The controller is first designed for the process P (s ). The design gives the controller parameter T d . An ideal controller for the process P (s )/(1+sT d /N )2 is then designed giving a new value of T d
etc. Such a procedure will also give a clear picture of the tradeoff between performance and filtering.
Set Point Weighting
When using the control law given by （6.1） it follows that a step change in the reference signal will result in an impulse in the control signal. This is often highly undesirable there for derivative action is frequently not applied to the reference signal. This problem can be avoided by filtering the reference value before feeding it to the controller. Another possibility is to let proportional action act only on part of the reference signal. This is called set point weighting. A PID controller given by （6.1） then becomes
()()()()()()???
? ????? ??-++-=?dt t dy dt t dr c d e t y t br K t u T T d t
i 01ττ 6.4
where b and c are additional parameter. The integral term must be based on error feedback to ensure the desired steady state. The controller given by D6.4E has a structure with two degrees of freedom because the signal path from y to u is different from that from r to u . The transfer function from r to u is
()()
()???
? ??++=
=
T T c d i
r cs s b K s s R s U 1
6.5
Time t
Figure 6.4 Response to a step in the reference for systems with different set point weights b = 0 dashed, b = 0.5 full and b =1.0 dash dotted. The process has the transfer function P （s ）=1/（s +1）3 and the controller parameters are k = 3, k i = 1.5 and k d = 1.5.
and the transfer function from y to u
is
()
()()???
?
??++==T T c d i y s s K s s R s U 1
1 6.6
Set point weighting is thus a special case of controllers having two degrees of freedom.
The system obtained with the controller （6.4） respond to load disturbances and measurement noise in the same way as the controller （6.1） . The response to reference values can be modified by the parameters b and c . This is illustrated in Figure 6.4, which shows the response of a PID controller to set-point changes, load disturbances, and measurement errors for different values of b . The figure shows clearly the effect of changing b . The overshoot for set-point changes is smallest for b = 0, which is the case where the reference is only introduced in the integral term, and increases with increasing b .
The parameter c is normally zero to avoid large transients in the control signal due to sudden changes in the set-point.
6.4 Different Parameterizations
The PID algorithm given by Equation （6.1）can be represented by the transfer function
()???
?
?
?++=T T d i s s K s G 1
1 6.7
T T T
i
d
i
K K '
''+'=
6.8
T T
T
d
i
i
''+= 6.9
T T T T T
d
i
d
i
d
'
'''+=
An interacting controller of the form Equation D6.8E that corresponds to a non-interacting controller can be found only if
T
T d
i
''
>4
The parameters are then given by
()T T
i
d
K
K 4112
-+=
'
()T T
T
T i
d
i
i
4112
-+
=
'
6.10
()T T
T
T i
d
i
d
4112
--
=
'
The non-interacting controller given by Equation （6.7） is more general, and we will use that in the future. It is, however, sometimes claimed that the interacting controller is easier to tune manually.
It is important to keep in mind that different controllers may have different structures when working with PID controllers. If a controller is replaced by another type of controller, the controller parameters may have to be changed. The interacting and the non-interacting forms differ only when both I and the D parts of the controller are used. If we only use the controller as a P, PI, or PD controller, the two forms are equivalent. Y et another representation of the PID algorithm is given by
()k k
d
i
s s
k s G ++
='' 6.11
The parameters are related to the parameters of standard form through
K
k =
T
k
i
i
K
=
T k
d d
K =
The representation Equation （6.11） is equivalent to the standard form, but the parameter values are quite different. This may cause great difficulties for anyone who is not aware of the differences, particularly if parameter 1/k i is called integral time and k d derivative time. It is even more confusing if k i is called integration time. The form given by Equation （6.11） is often useful in analytical calculations because the parameters appear linearly. The representation also has the advantage that it is possible to obtain pure proportional, integral, or derivative action by finite values of the parameters.
第6章 PID 控制
6.1 介绍
PID 控制器是反馈控制的最常见形式。因为早在40年代它就成为了过程控制的标准工具。在今天的过程控制业中, 超过95%的控制回路是PID 类型, 多数实际上是PI 控制。PID 控制是分布控制系统的一种重要组成部分。控制器被隐藏在许多其他控制系统下面。PID 控制与逻辑控制经常结合在一起,连续作用、选择器, 和简单的功能模块一起构成复杂自动化系统，可以应用在发电, 运输，以及制造业。许多经典的控制策略, 譬如模型有预测性的控制。PID 控制是使用在要求水平较低的场合；PID 控制器应用在底层。PID 控制器在每个控制工程师的应用实例里都能经常见到。
近年来PID 控制器在技术生产上也产生了许多变化, 从机械到微处理器控制由电子管, 晶体管,组合电路组成的控制系统。 微处理器对PID 控制器有着强烈的影响。实际上今天制作的所有PID 控制器都是建立在微处理器的基础上的。这就有机会扩展其他的特点：像自动定调, 获取预定, 和连续的适应。
6.2 算法
我们开始讲解PID 控制器的主要特点。 PID 算法的描述:
()()()()???
?
?
?++=?dt t de d e t e K t u T T d
t
i 01
ττ 6.1
这里 y 是被测量的处理可变量, r 参考可变量, u 是控制信号，e 是控制误差
??
? ?
?-
=
y y
sp
e 。参考变量经常可以被称为是固定的点。控制信号包含三个量，作一下改变，即可预测下一时间的走向问题。
PID 的作用
图6.1说明的是典型的比例控制. 控制器给定Ti=∞，Td=0。表示在比例控制中总存在有一种稳定状态误差。获取值增加误差将减少, 但系统稳定性将受到影响。
图 6.2 说明增加积分式的作用。它跟随图6.1而来增加时间Ti .当积分式运行使用。稳定状态误差将逐渐的消失。相比较，说明在图6.3减少Ti ，波动继续增大.
图 6.3 举例说明增加输出的方法的效果。 参数 K 和 Ti 被选定以便闭环系统是振动的。当输出时间过长时，导出时间将被阻值再一次增加，减少也是一样。当在时间Td 作线形补偿取消输出可以得到预测的结果。用简单的方法解释，
如果预测时间Td太大，导出将没有影响。在图6.3中，振荡的周期是没有引出的，大约是6S。
图6.1
图6.2.
当Td比1S（六分之一的周期时间）大的时候，输出的作用停止是有效的。也要注意当输出时间增加的时候，振荡的周期也将增加。
图6.1说明有许多比PID更好的系统，但是，实际上一个好控制器，必需得有一个好的PID控制器。而获得一个好的PID控制器，也需要认真地考虑一下。
图6.3.
? 噪声过滤和高频率关闭 ? 凝固点衡量和2 DOF ? 终结 ? 调谐
? 计算机执行
在使用PID 控制器的时候，有些问题就会涌现出来，但他们实际上最重要的是在所有控制中的实施。许多问题与反馈本身是紧密地联系在一起的。其中，有些在早期的一些资料中就已经被研究过。
6.3 过滤和凝固点的衡量
微分对噪声总是敏感的。像G(s) = s 的微分器。以下的例子可以有力的说明。 例子6.1-DIFFERENTIATION 放大高频率噪音，参考信号
()()t t t n t t y n
n
a
ω
sin
sin sin +
=+=
这里的噪声是正弦信号，频率为w 。信号的导数是
()()t
t t n t dt
t dy n n
a
ωcos cos cos +
=+=
针对噪音的信号比率为原始的信号是1倍，但噪音的信号比率是被区分的。如果w 是足够大的这个比率是可能任意提高的。
从一种积分作用控制器来看，是有必要限制积分范围的，以得到高频率。这可以由做积分的范围决定
N
s s
D T
KT d
d +-
=1 6.2
替换D=sT d Y 。由（6.2）的f 得到的近似值，可以解释为理想的积分sT d 过滤了由一个以时间常数Td/N 的优先处理的系统。近似值以一种低频率信号组分。但是,这种获取,限制了KN 。这就意味着, 高频率测量噪声大多由因素KN 被放大，N 的典型的价值是8 到20 。
高频率获取的进一步
测量y 对控制装置输出u 的一种PID 控制器与近似积分是
()???
?
?
?++
+
-=N s s S K S C T
KT T d
d
I
11
1 这种控制器有稳定的输入
()()N s C K s +-=∞
→1lim
由于在高频率， 因而决定从强度问题讨论,这可能有另外达到低通过滤的控
制信号
()()
T s s F f n
+=
11
这里T f 是过滤器时间常数。T f 选择在过滤的范围和起点之间。如果选择时间，T f ＝T d /N 是适当的选择。如果控制器是唯一的PI ，Tf ＝T i /N 可能是适当的。 控制器也可能被实施，就像
()()
N T s T T d
s s K s C d i
+???
? ?
?++-=12
1
1
1 6.3
这个结构的好处, 我们能将其开发设计为一种理想的PID 控制器和使用一种设计程序方法。控制器首先被设计为处理P(s)，设计给控制器参量Td ，一种理想的控制器为P(s)/(1+sT d /N)2 ，然后重新给Td 符值，这样做将有一张清楚的滤波图片。
设定点权衡
当使用（6.1）提供的控制规则时，参考信号的变化会在控制信号上产生波动。这种情况是不好的，因此，输出的信号被用于参考信号。这个问题应在控制器设计前考虑，得以避免，另外的一种可能性在参考信号的部份上，这叫做关键点权衡。PID 控制器由 (6.1)提供，然后写成
()()()()()()???
?
????? ??-++-=?dt t dy dt t dr c d e t y t br K t u T T d t
i 01ττ 6.4
b 和
c 是另外的参量，必须根据误差反馈来保证期望的稳定状态。控制器由
（6.4.）提供，因为从y 到u 的信号路径与 r 到u 不同，所以用自由结构，从r 到u 。
()()
()???
? ??++=
=
T T c d i
r cs s b K s s R s U 1 6.5
Time t
图 6.4 在参考系统以另外固定点衡量b=0, b=0.5和b=1.0。传递作用P(s)=1/(s+1)3 和控制器参量是k = 3, ki = 1.5 和kd = 1.5 。
并且传递作用从y 到u 是
()()
()???
? ??++=
=
T T c d i
y s s K s s R s U 11 6.6
定点衡量是因为控制器是一种特殊二阶系统。
控制器(6.4) 反应加载干扰和测量噪声以与控制器相似。在表6.4说明对参考价的反应可能被参量b 和c 修改。展示PID 控制器对信号一点的变动的反应,增加扰动, 并且在b 的不同的值计量误差。图清楚地显示改变b 的作用。信号点的变动是在b = 0,
参量c 通常是零，为避免由于瞬间在信号一点上的突然的变化。 6.4 不同参数
PID 算法由方程式 (6.1)提供，并且传递函数的代表为
()???
? ?
?++=T T d i
s s K s G 1
1 6.7
T T T i
d
i
K K
'
''+'
= 6.8
T T
T
d
i
i
''+= 6.9
T T T T T
d
i
d
i
d
'
'''+=
对应于控制器形式，可导出
T
T d
i
''
>4
给定参量
()T T
i
d
K K 4112
-+
=
'
()T T
T
T i
d
i
i
4112
-+
=
'
6.10
()T T
T
T i
d
i
d
4112
--
=
'
控制器方程式 (6.7) 只是一般的, 并且我们正在使用。但是, 有些控制器更加容易手工调节。
重点记住, 不同的控制器也许有不同的结构，当工作与PID 控制器合作的时候，不同的控制器可能有不同的结构。（在与 PID 控制器合作的时候，不同的控制器可能有不同的结构。）如果控制器由其它类型控制器替换, 控制器参数必须被改变。
当控制器的I 和D 部分被使用。 如果我们只使用控制器作为P, PI, 或PD 控制器, 二个形式是等效的。PID 算法的另一个表示法
()k k
d
i
s s
k s G ++
='' 6.11
参量与标准格式的转换
K
k =
T
k
i
i
K
=
T k
d d
K =
方程（6.11）是对标准形式的变换，但是参数值是不同的。这可能为任何一个有不同观点，或者不了解的人带来很大的困难，特别是如果参数1/ki 整体的时间和k d 导出时间，如果k i 叫做整合时间，会更难以理解。由于参数线的出现，所以时常方程（6.11）这种形式在分析计算中经常用到。这种表示法也有好处, 它可能获得纯比例, 积分式。
Fuzzy Logic Based Autonomous Skid Steering Vehicle Navigation L.Doitsidis,K.P.Valavanis,N.C.Tsourveloudis Technical University of Crete Department of Production Engineering and Management Chania,Crete,Greece GR-73100 {Idoitsidis ,kimonv,nikost}@dpem.tuc.gr Abstract-A two-layer fuzzy logic controller has been designed for 2-D autonomous Navigation of a skid steering vehicle in an obstacle filled environment. The first layer of the Fuzzy controller provides a model for multiple sonar sensor input fusion and it is composed of four individual controllers, each calculating a collision possibility in front, back, left and right directions of movement. The second layer consists of the main controller that performs real-time collision avoidance while calculating the updated course to be applicability and implementation is demonstrated through experimental results and case studies performed o a real mobile robot. Keywords - Skid steering, mobile robots, fuzzy navigation. Ⅰ.INTRODUCTION The exist several proposed solutions to the problem of autonomous mobile robot navigation in 2-D uncertain environments that are based on fuzzy logic[1],[2],evolutionary algorithms [3],as well as methods combining fuzzy logic with genetic algorithms[4] and fuzzy logic with electrostatic potential fields[5]. The paper is the outgrowth of recently published results [9],[10],but it studies 2-D environments navigation and collision avoidance of a skid steering vehicle. Skid steering vehicles are compact, light, require few parts to assemble and exhibit agility from point turning to line driving using only the motions, components, and swept volume needed for straight line driving. Skid steering vehicle motion differs from explicit steering vehicle motion in the way the skid steering vehicle turns. The wheels rotation is limited around one axis and the back of steering wheel results in navigation determined by the speed change in either side of the skid steering vehicle. Same speed in either side results in a straight-line motion. Explicit steering vehicles turn differently since the wheels are moving around two axes. The geometric configuration of a skid steering vehicle in the X-Y plane is shown in Fig1，while a t is the heading angle, W is the robot width, θthe sense of rotation and S1, S2 are the speeds in the either side of the robot. The derived and implemented planner a two-layer fuzzy logic based controller that provides purely” reactive behavior” of the vehicle moving in a 2-D obstacle filled environment, with inputs readings from a ring of 24 sonar sensors and angle errors, and outputs the updated rotational and translational velocities of the vehicle. Ⅱ.DESIGN OF THE FUZZY LOGIC CONTROL SYSTEM
一、材质中英文对照表 1.摇粒绒：Polar Fleece 2.珊瑚绒：coral fleece / soft terry 3.羊羔绒：Berber fleece / polyester faux sherpa 4.短毛绒：short plush 5.长毛绒：long plush 6.毛绒：fur 7.天鹅绒：velvet 8.拖把绒：cord velour 9.PV绒：PV plush 10.毛巾布：terry 11.灯芯绒：corduroy 12.双色毛绒：two-tone faux fur 13.毛线针织：knitting 14.麂皮绒：microfiber/microsuede 15.格利特：glitter 16.亮片：sequin 17.佳积布：nylex 18.尼龙布：nylon 19.汗衫布：jersey 20.沙丁布：satin 21.网布：mesh 22.帆布：canvas 23.斜纹棉布: cotton twill 24.PU 25.镜面PU：patent PU 26.平纹PU：smooth PU 27.EV A 28.点塑底：fabric with dot / skid free dot/non skid dot 29.TPR 30.PVC注塑：PVC injected 二、鞋子装饰物（ornament） 1.松紧带：elastic gore 2.魔术贴：velcro 3.电绣：embroidery 4.蝴蝶结：bow 5.爱心：heart 6.鞋带：lace 7.鞋眼：eyelet 8.人造钻石：rhinestone 9.搭带：strap 10.拉环：loop 11.毛球：POM 12.织唛标：Woven label 13.烫印：heat seal 14.贴片：patch 15.拉链：zipper 三、颜色 1. 豹纹：leopard/ cheetah 2. 斑马纹：zebra 3. 虎纹：tiger 4. 米黄色：beige 5. 桃红色：fuschia 6. 淡紫色：lilac 7. 海军蓝：Navy 8. 咖啡色：Brown 9. 迷彩：camo 10. 湖水蓝：blue atoll/ turq / lake blue 11. 格子：plaid / gingham 12. 紫色：purple 13. 灰色：grey/gray 14. 条纹：strip 15. 银光粉：neon pink 16. 金属色：metallic 17. 栗色：chestnut AI Artwork 设计稿
常用单位的中英文对照翻译 单位 Unit. 单位制 system of units 米 meter (m) 毫米 millimeter (mm) 英尺 foot (ft) 英寸 inch (in) 弧度 radian (rad) 度degree (°) 摄氏 Celsius. (C) 华氏 Fahrenheit (F) 磅／平方英寸 pounds per square inch (psi) 百万帕斯卡 million pascal (MPa) 巴 bar 千克（公斤） kilogram (kg) 克 gram (g) 牛顿 newton (N) 吨 ton (t) 千磅 kilopound (kip) 平方米 square meter (m 2) 方毫米 square millimeter (mm2 ) 立方米 cubic meter (m3 ) 升 liter; litre (L) 转／分 revolutions per minute (rpm) 百万分之一 parts per million (ppm) 焦（耳） Joule (J) 千瓦 kilowatt (kW) 伏（特） volt (V) 安（培） ampere (A) 欧（姆）ohm (Ω) （小）时 hour (h) 分 minute (min) 秒 second (s)
管道组成件专业英语(中英文对照) 1 管道组成件 Piping component 1.1 管子 Pipe 管子(按照配管标准规格制造的) pipe 管子(不按配管标准规格制造的其他用管) tube 钢管 steel pipe 铸铁管 cast iron pipe 衬里管 lined pipe 复合管 clad pipe 碳钢管 carbon steel pipe 合金钢管 alloy steel pipe 不锈钢 stainless steel pipe 奥氏体不锈钢管 austenitic stainless steel pipe 铁合金钢管 ferritic alloy steel pipe 轧制钢管 wrought-steel pipe 锻铁管 wrought-iron pipe 无缝钢管 seamless (SMLS) steel pipe 焊接钢管 welded steel pipe 电阻焊钢管 electric-resistance welded steel pipe 电熔（弧）焊钢板卷管 electric-fusion (arc)-welded steel-plate pipe 螺旋焊接钢管 spiral welded steel pipe 镀锌钢管 galvanized steel pipe 热轧无缝钢管 hot-rolling seamless pipe 冷拔无缝钢管 cold-drawing seamless pipe 水煤气钢管 water-gas steel pipe 塑料管 plastic pipe 玻璃管 glass tube 橡胶管 rubber tube 直管 run pipe; straight pipe 1.2 管件 Fitting 弯头 elbow 异径弯头 reducing elbow 带支座弯头 base elbow k半径弯头 long radius elbow 短半径弯头 short radius elbow
外文资料 Numerical Control One of the most fundamental concepts in the area of advanced manufacturing technol-ogies is Numerical Control(NC). Prior to the advent of NC, all machine tools were manually operated and controlled. Among the many limitations associated with manual control machine tools. Perhaps none is more prominent than the limitation of operator skills. With manual control, the quality of the product are directly related to and limited to the skills of the operator. Numerical Control represented the first major step away from human control of machine tools. Numerical Control means the control of machine tools and other manufacturing systems through the use of prerecorded, written symbolic instructions. Rather than operating a machine tool. For a machine tool to be numerically controlled, it must be interfaced with a device for accepting and decoding the programmed instructions, known as a reader. Numerical Control was developed to overcome the limitation of human operators, and it has done so. Numerical Control machines are more accurate than manually operated machines,they can produce parts more uniformly, they are faster, and the long-run tooling costs are lower. The development of NC led to the development of several other innovations in manufacturing technology: (1) Electrical discharge machining. (2) Laser cutting. (3) Electron beam welding. Numerical Control has also made machine tools more versatile than their manually operated predecessors. An NC machine tool can automatically produce a wide variety of parts, each involving an assortment of widely varied and comples machining processes. Numerical Control has allowed manufacturers to undertake the production of products that would not have been feasible from an economic perspective using manually controlled machine tools and processes.
1998年的IEEE 国际会议上机器人及自动化 Leuven ，比利时1998年5月 一种实用的办法--带拖车移动机器人的反馈控制 F. Lamiraux and J.P. Laumond 拉斯，法国国家科学研究中心 法国图卢兹 {florent ,jpl}@laas.fr 摘要 本文提出了一种有效的方法来控制带拖车移动机器人。轨迹跟踪和路径跟踪这两个问题已经得到解决。接下来的问题是解决迭代轨迹跟踪。并且把扰动考虑到路径跟踪内。移动机器人Hilare的实验结果说明了我们方法的有效性。 1引言 过去的8年，人们对非完整系统的运动控制做了大量的工作。布洛基[2]提出了关于这种系统的一项具有挑战性的任务，配置的稳定性，证明它不能由一个简单的连续状态反馈。作为替代办法随时间变化的反馈[10,4,11,13,14,15,18]或间断反馈[3]也随之被提出。从[5]移动机器人的运动控制的一项调查可以看到。另一方面，非完整系统的轨迹跟踪不符合布洛基的条件，从而使其这一个任务更为轻松。许多著作也已经给出了移动机器人的特殊情况的这一问题[6,7,8,12,16]。 所有这些控制律都是工作在相同的假设下：系统的演变是完全已知和没有扰动使得系统偏离其轨迹。很少有文章在处理移动机器人的控制时考虑到扰动的运动学方程。但是[1]提出了一种有关稳定汽车的配置，有效的矢量控制扰动领域，并且建立在迭代轨迹跟踪的基础上。 存在的障碍使得达到规定路径的任务变得更加困难，因此在执行任务的任何动作之前都需要有一个路径规划。 在本文中，我们在迭代轨迹跟踪的基础上提出了一个健全的方案，使得带拖车的
机器人按照规定路径行走。该轨迹计算由规划的议案所描述[17]，从而避免已经提交了输入的障碍物。在下面，我们将不会给出任何有关规划的发展，我们提及这个参考的细节。而且，我们认为，在某一特定轨迹的执行屈服于扰动。我们选择的这些扰动模型是非常简单，非常一般。它存在一些共同点[1]。 本文安排如下：第2节介绍我们的实验系统Hilare及其拖车：两个连接系统将被视为（图1）。第3节处理控制方案及分析的稳定性和鲁棒性。在第4节，我们介绍本实验结果。 图1带拖车的Hilare 2 系统描述 Hilare是一个有两个驱动轮的移动机器人。拖车是被挂在这个机器人上的，确定了两个不同的系统取决于连接设备：在系统A的拖车拴在机器人的车轮轴中心线上方（图1 ，顶端），而对系统B是栓在机器人的车轮轴中心线的后面（图1 ，底部)。A l= 0 。这个系统不过单从控制的角度来看，需要更对B来说是一种特殊情况，其中 r 多的复杂的计算。出于这个原因，我们分开处理挂接系统。两个马达能够控制机器人的线速度和角速度（v r，r ω)。除了这些速度之外，还由传感器测量，而机器人和拖车之间的角度?，由光学编码器给出。机器人的位置和方向（x r,y r,rθ）通过整合前的速度被计算。有了这些批注，控制系统B是：
中文姓氏的英文翻译对照表 中文姓氏的英文翻译对照表.txt我们用一只眼睛看见现实的灰墙，却用另一只眼睛勇敢飞翔，接近梦想。男人喜欢听话的女人，但男人若是喜欢一个女人，就会不知不觉听她的话。在互联网上混的都时兴起个英文名字，一是方便注册用户名，二是有个好英文名容易显得自己比较Cool。但是起英文名时，中文姓氏还是要保留的，并且姓氏一般都有专门的英文翻译，比如“刘德华”的英文名是Andy，刘姓对应的英文翻译是Lau，所以全称便是“Andy Lau”。当然了，我们一般人直接用汉语拼音作为姓氏的英文翻译也可以，但在比较正式的场合下，最好还是用相应的英文翻译。 姓氏的英文翻译跟汉语拼音是有一些细微差别的，这主要由中西方人发音的不同特点来决定的。比如，从声母上来看，D开头的姓，英文翻译对应的是T，G对应的是K，X对应的是HS，Z、J 一般对应的是C，韵母也会有一些细微差别。详细的，请参考如下中文姓氏的英文翻译对照表，正在起英文名的朋友可以看看。 A: 艾--Ai 安--Ann/An 敖--Ao B: 巴--Pa 白--Pai 包/鲍--Paul/Pao 班--Pan 贝--Pei 毕--Pih 卞--Bein 卜/薄--Po/Pu 步--Poo 百里--Pai-li C: 蔡/柴--Tsia/Choi/Tsai 曹/晁/巢--Chao/Chiao/Tsao 岑--Cheng 崔--Tsui 查--Cha
常--Chiong 车--Che 陈--Chen/Chan/Tan 成/程--Cheng 池--Chi 褚/楚--Chu 淳于--Chwen-yu D: 戴/代--Day/Tai 邓--Teng/Tang/Tung 狄--Ti 刁--Tiao 丁--Ting/T 董/东--Tung/Tong 窦--Tou 杜--To/Du/Too 段--Tuan 端木--Duan-mu 东郭--Tung-kuo 东方--Tung-fang E: F: 范/樊--Fan/Van 房/方--Fang 费--Fei 冯/凤/封--Fung/Fong 符/傅--Fu/Foo G: 盖--Kai 甘--Kan 高/郜--Gao/Kao 葛--Keh 耿--Keng 弓/宫/龚/恭--Kung 勾--Kou 古/谷/顾--Ku/Koo 桂--Kwei
1. The Dragon Boat Festival, also called the Duanwu Festival, is celebrated on the fifth day of the fifth month according to the Chinese calendar. For thousands of years, the festival has been marked by eating Tzung Tzu and racing dragon boats. The most important activities of this festival are the dragon boat races. Competing teams drive their colorful dragon boats forward to the rhythm（节奏,韵律） of beating drums. These exciting races were inspired by the villager's attempts to rescue Chu Yuan from the Mi Lo River. This tradition has remained unbroken for centuries. A very popular dish during the Dragon Boat festival is tzung tzu. This tasty dish consists of rice dumplings with meat, peanut, egg yolk（蛋黄）, or other fillings wrapped in bamboo leaves. The tradition of tzung tzu is meant to remind us of the village fishermen scattering rice across the water of the Mi Low River in order to appease the river dragons so that they would not devour Chu Yuan. 一，端午节 龙舟节也叫做端午节，它位于每年农历的五月初五，经过几千年的时间，划龙舟和吃粽子已经成为这个节日的标志。 端午节最重要的活动是龙舟竞赛，比赛的队伍在热烈的鼓声中划著他
附录 Abstract: Programmable controller in the field of industrial control applications, and PLC in the application process, to ensure normal operation should be noted that a series of questions, and give some reasonable suggestions. Key words: PLC Industrial Control Interference Wiring Ground Proposal Description Over the years, programmable logic controller (hereinafter referred to as PLC) from its production to the present, to achieve a connection to the storage logical leap of logic; its function from weak to strong, to achieve a logic control to digital control of progress; its applications from small to large, simple controls to achieve a single device to qualified motion control, process control and distributed control across the various tasks. PLC today in dealing with analog, digital computing, human-machine interface and the network have been a substantial increase in the capacity to become the mainstream of the field of control of industrial control equipment, in all walks of life playing an increasingly important role. ⅡPLC application areas Currently, PLC has been widely used in domestic and foreign steel, petroleum, chemical, power, building materials, machinery manufacturing, automobile, textile, transportation, environmental and cultural entertainment and other industries, the use of mainly divided into the following categories: 1. Binary logic control Replace traditional relay circuit, logic control, sequential control, can be used to control a single device can also be used for multi-cluster control and automation lines. Such as injection molding machine, printing machine, stapler machine, lathe, grinding machines, packaging lines, plating lines and so on. 2. Industrial Process Control In the industrial production process, there are some, such as temperature, pressure, flow, level and speed, the amount of continuous change (ie, analog), PLC using the appropriate A / D and D / A converter module, and a variety of control algorithm program to handle analog, complete closed-loop control. PID closed loop control system adjustment is generally used as a conditioning method was more. Process control in metallurgy, chemical industry, heat treatment, boiler control and so forth have a very wide range of applications 3. Motion Control PLC can be used in a circular motion or linear motion control. Generally use a dedicated motion control module, for example a stepper motor or servo motor driven single-axis or multi-axis position control module, used in a variety of machinery, machine tools, robots, elevators and other occasions. 4. Data Processing PLC with mathematics (including matrix operations, functions, operation, logic operation), data transfer, data conversion, sorting, look-up table, bit manipulation functions, you can complete the data collection, analysis and processing.Data
模糊控制理论 概述 模糊逻辑广泛适用于机械控制。这个词本身激发一个一定的怀疑,试探相当于“仓促的逻辑”或“虚假的逻辑”,但“模糊”不是指一个部分缺乏严格性的方法,而这样的事实,即逻辑涉及能处理的概念,不能被表达为“对”或“否”,而是因为“部分真实”。虽然遗传算法和神经网络可以执行一样模糊逻辑在很多情况下,模糊逻辑的优点是解决这个问题的方法,能够被铸造方面接线员能了解,以便他们的经验,可用于设计的控制器。这让它更容易完成机械化已成功由人执行。 历史以及应用 模糊逻辑首先被提出是有Lotfi在加州大学伯克利分校在1965年的一篇论文。他阐述了他的观点在1973年的一篇论文的概念,介绍了语言变量”,在这篇文章中相当于一个变量定义为一个模糊集合。其他研究打乱了,第二次工业应用中,水泥窑建在丹麦,即将到来的在线1975。 模糊系统在很大程度上在美国被忽略了,因为他们更多关注的是人工智能，一个被过分吹嘘的领域，尤其是在1980年中期年代,导致在诚信缺失的商业领域。 然而日本人对这个却没有偏见和忽略，模糊系统引发日立的Seiji Yasunobu和Soji Yasunobu Miyamoto的兴趣。,他于1985年的模拟,证明了模糊控制系统对仙台铁路的控制的优越性。他们的想法是被接受了,并将模糊系统用来控制加速、制动、和停车，当线于1987年开业。 1987年另一项促进模糊系统的兴趣。在一个国际会议在东京的模糊研究那一年,Yamakawa论证<使用模糊控制,通过一系列简单的专用模糊逻辑芯片,在一个“倒立摆“实验。这是一个经典的控制问题,在这一过程中,车辆努力保持杆安装在顶部用铰链正直来回移动。 这次展示给观察者家们留下了深刻的印象,以及后来的实验,他登上一Yamakawa酒杯包含水或甚至一只活老鼠的顶部的钟摆。该系统在两种情况下,保持稳定。Yamakawa最终继续组织自己的fuzzy-systems研究实验室帮助利用自己的专利在田地里的时候。
1.Sales Agreement The agreement, (is) made in Beijing this eighth day of August 1993 by ABC Trading Co., Ltd., a Chinese Corporation having its registered office at Beijing, the People’ Repubic of China (hereinafter called “Seller”) and International Trading Co., Ltd., a New York Corporation having its registered office at New York, N.Y., U.S.A. (hereinafter called “Buyer”). 2.WITNESSETH WHEREAS, Seller is engaged in dealing of (product) and desires to sell (product)to Buyer, and WHEREAS, Buyer desires to purchase(product) from Sellers, Now, THEREFORE, it is agreed as follows: 3.Export Contract th This Contract is entered into this 5 day of August 1993 between ABC and Trading Co., Ltd. (hereinafter called “Seller”) who agrees to sell, and XYZ Trading Co., Ltd. (hereinafter called “Buyer”) who agrees to buy the following goods on the following terms and condition. 4.Non-Governmental Trading Agreement No. ＿＿ This Agreement was made on the＿day of＿ 19＿, BETWEEN ＿(hereinafter referred to as the Seller) as the one Side and ＿ (hereinafter referred to as the Buyer) as the one other Side. WHEREAS, the
美国学校提供的学位有很多种，依所学领域的不同，而有不同的学位。以下列出的是美国高等教育中较常见的学位: Ph.D.（Doctor of Philosophy）: 博士学位。而有些领域的博士课程会有不同的学位名称，如D.A.(Doctor of Arts)、Ed.D.(Doctor of Education) M.B.A.（Master of Business Administration）: 商学管理硕士。 M.A.（Master of Arts）硕士；B.A.（Bachelor of Arts）学士: 两者皆属于人文、艺术或社会科学的领域，如文学、教育、艺术、音乐。 M.S.（Master of Science）硕士；B.S.（Bachelor of Science）学士: 两者皆属于理工、科学的领域，如数学、物理、信息等。 Associate Degree（副学士学位）: 读完两年制小区大学或职业技术学校所得到的学位。 Dual Degree（双学位）: 是由两个不同学院分别授与，因此得到的是两个学位。 Joint Degree：为两个不同学院联合给予一个学位，如法律经济硕士。 major 主修 minor 辅修 大家要搜索自己的专业, 请按 ctrl + F 打开搜索窗口, 然后输入关键字查询 学士 Bachelor of Arts B.A. 文学士 Bachelor of Arts in Education B.A.Ed., B.A.E. 教育学文学士 Bachelor of Arts in Computer Science B.A.CS 计算机文学士 Bachelor of Arts in Music B.A.Mus,B.Mus 音乐艺术学士 Bachelor of Arts in Social Work B.A.S.W 社会工作学文学士 Bachelor of Engineering B.Eng., B.E 工学士 Bachelor of Engineering in Social Science B.Eng.Soc 社会工程学士 Bachelor of Engineering in Management B.Eng.Mgt 管理工程学士 Bachelor of Environmental Science/Studies B.E.Sc., B.E.S 环境科学学士 Bachelor of Science B.S 理学士 Bachelor of Science in Business B.S.B., B.S.Bus 商学理学士 Bachelor of Science in Business Administration B.S.B.A 工商管理学理学士 Bachelor of Science in Education B.S.Ed., B.S.E 教育学理学士 Bachelor of Science in Engineering B.S.Eng., B.S.E 工程学理学士 Bachelor of Science in Forestry B.S.cF 森林理学士 Bachelor of Science in Medicine B.S.Med 医学理学士 Bachelor of Science in Medical Technology B.S.M.T., B.S.Med.Tech 医技学理学士 Bachelor of Science in Nursing B.S.N., B.S.Nurs 护理学理学士 Bachelor of Science in Nutrition B.SN 营养学理学士 Bachelor of Science in Social Work B.S.S.W 社会工作学理学士 Bachelor of Science in Technology B.S.T 科技学理学士 Bachelor of Computer Science B.CS 计算机理学士 Bachelor of Computer Special Science B.CSS 计算机特殊理学士 Bachelor of Architecture B. Arch. 建筑学士 Bachelor of Administration B.Admin. 管理学士
英文原文 Introductions to Control Systems Automatic control has played a vital role in the advancement of engineering and science. In addition to its extreme importance in space-vehicle, missile-guidance, and aircraft-piloting systems, etc, automatic control has become an important and integral part of modern manufacturing and industrial processes. For example, automatic control is essential in such industrial operations as controlling pressure, temperature, humidity, viscosity, and flow in the process industries; tooling, handling, and assembling mechanical parts in the manufacturing industries, among many others. Since advances in the theory and practice of automatic control provide means for attaining optimal performance of dynamic systems, improve the quality and lower the cost of production, expand the production rate, relieve the drudgery of many routine, repetitive manual operations etc, most engineers and scientists must now have a good understanding of this field. The first significant work in automatic control was James Watt’s centrifugal governor for the speed control of a steam engine in the eighteenth century. Other significant works in the early stages of development of control theory were due to Minorsky, Hazen, and Nyquist, among many others. In 1922 Minorsky worked on automatic controllers for steering ships and showed how stability could be determined by the differential equations describing the system. In 1934 Hazen, who introduced the term “ervomechanisms”for position control systems, discussed design of relay servomechanisms capable of closely following a changing input. During the decade of the 1940’s, frequency-response methods made it possible for engineers to design linear feedback control systems that satisfied performance requirements. From the end of the 1940’s to early 1950’s, the root-locus method in control system design was fully developed. The frequency-response and the root-locus methods, which are the