PID中英文对照翻译(共13页).doc

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1、精选优质文档-倾情为你奉上中英文互译PID ControlIntroductionThe 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, mos

2、t 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 ingredi

3、ent 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

4、, 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

5、 the “bread and butter of control engineering. It is an important component in every control engineers 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 ha

6、d 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 The AlgorithmWe will start by summarizing the key feat

7、ures of the PID controller. The “textbook” version of the PID algorithm is described by: 6.1where y is the measured process variable, r the reference variable, u is the control signal and e is the control error（e = y）. The reference variable is often called the set point. The control signal is thus

8、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 Ti, and derivative time Td.

9、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 dif

10、ferent 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 ActionProportional control is illustrated in Figure 6.1. The controller is given by D6.1E with Ti = and Td=0. The

11、 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 inc

12、reases with decreasing integral time Ti. 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 Ti. The properties of derivative ac

13、tion are illustrated in Figure 6.3.Figure 6.3 illustrates the effects of adding derivative action. The parameters K and Ti 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 t

14、hat derivative action can be interpreted as providing prediction by linear extrapolation over the time Td. Using this interpretation it is easy to understand that derivative action does not help if the prediction time Td is too large. In Figure 6.3 the period of oscillation is about 6 s for the syst

15、em without derivative Chapter 6. PID ControlFigure 6.1Figure 6.2 Derivative actions cease to be effective when Td 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 PerspectiveThere is much more to PID than is reve

16、aled 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 implementationIn the ca

17、se 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

18、Filtering and Set Point WeightingDifferentiation 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.where the noise is sinusoidal noise with frequency w. The derivati

19、ve of the signal isThe signal to noise ratio for the original signal is 1/an but the signal to noise ratio of the differentiated signal is w/an. 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 gai

20、n of the derivative term. This can be done by implementing the derivative term as 6.2instead of D=sTdY. The approximation given by (6.2) can be interpreted as the ideal derivative sTd filtered by a first-order system with the time constant Td/N. The approximation acts as a derivative for low-frequen

21、cy 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 gainThe transfer function from measurement y to controller output u of a PID contr

22、oller with the approximate derivative isThis controller has constant gainat 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 additionallow

23、pass filtering of the control signal bywhere Tf is the filter time constant and n is the order of the filter. The choice of Tf 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 abo

24、ve. If the derivative time is used, T f= Td/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 6.3This structure has the advantage that we can develop the design methods for an ideal PID controller and use an iterative design pro

25、cedure. The controller is first designed for the process P(s). The design gives the controller parameter Td. An ideal controller for the process P(s)/(1+sTd/N)2 is then designed giving a new value of Td etc. Such a procedure will also give a clear picture of the tradeoff between performance and filt

26、ering.Set Point WeightingWhen 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 b

27、e 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 6.4where b and c are additional parameter. The

28、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 6.5 Time tFigure 6.4 Response to a

29、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, ki = 1.5 and kd = 1.5.and the transfer function from y to u is 6.6Set point weighting is th

30、us 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

31、 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

32、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 ParameterizationsThe PID algorithm given by Equation（6.1）can be represented by the transfer func

33、tion 6.7 6.8 6.9 An interacting controller of the form Equation D6.8E that corresponds to a non-interacting controller can be found only ifThe parameters are then given by 6.10The non-interacting controller given by Equation （6.7） is more general, and we will use that in the future. It is, however,

34、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

35、 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. Yet another representation of the PID algorithm is given by 6.11The parameters are rel

36、ated to the parameters of standard form through 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/ki is called integral tim

37、e and kd derivative time. It is even more confusing if ki 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, integr

38、al, or derivative action by finite values of the parameters.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

39、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: 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 。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，P-term，I-t

40、erm，D-term，控制器的参数包括比例系数K，整体时间Ti，和Td。以过去，现在和未来为基础的控制轨迹可解释整体，比例项和输出部份的关系。图中举例。在不同时间的运动可以表示输出部分的一个典型的例子。在参数值方面作一下改变，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

41、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

42、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，参考信号这里的噪声是正弦信号，频率为w 。信号的导数是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

43、得到高频率。这可以由做积分的范围决定 6.2替换D=sTdY。由（6.2）的f得到的近似值，可以解释为理想的积分sTd 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这种控制器有稳定的输入由于在高频率， 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这里Tf 是过滤器时间常数。Tf选择在过滤的范围和起点之间。如果选择时间，T fTd/N是适当的选择。如果控制器是唯一的P

44、I，Tf Ti/N可能是适当的。控制器也可能被实施，就像 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+sTd/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 6.4b和c是另外的参量，必

45、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。 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 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

46、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 6.7 6.8 6.9 对应于控制器形式，可导出给定参量 6.10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 6.11参量与标准格式的转换 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kd导出时间，如果ki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。专心-专注-专业