模糊控制PID的调节-毕业论文外文翻译.doc

《模糊控制PID的调节-毕业论文外文翻译.doc》由会员分享，可在线阅读，更多相关《模糊控制PID的调节-毕业论文外文翻译.doc（8页珍藏版）》请在淘文阁 - 分享文档赚钱的网站上搜索。

1、 原文Tuning Of Fuzzy PID ControllersPID stands for Proportional, Integral and Derivative. The following explanations describe PID as it applies to the precise control of a process temperature. A process is an area or zone that is being controlled a t or driven to a precise temperature. PID is a contro

2、l method or mode that has three functions or variables. The proportional action dampens process response. The integral corrects for droop. Droop is the difference in temperature between the process set point and the actual process temperature. The set point is the desired process temperature. The de

3、rivatives minimize overshoot and undershoot. Overshoot is the amount in temperature units that the process temperature exceeds the set point before the process stabilizes. Process stabilization is achieved when the set point and process temperatures are equal over a defined period of time. Undershoo

4、t is the amount in temperature units that the process temperature falls below the set point before the process stabilizes.Proportional is the control output effort in proportion to the error from set point. A control output is a signal action delivered in response to the difference between set point

5、 and process temperature. An output usually controls a heating or cooling action. The proportional range is referred to as a “band” and is usually measured in temperature units. If a proportional band of 20 degrees were applied to a process that is 10 degrees below set point, the heat output would b

6、e 50 percent. The lower the proportional band, the higher the gain. Gain is the amount of amplification used in an electrical circuit. Proportional band is sometimes referred to as gain. The proportional band or PB is a range in which the proportioning function of the controller is active. The PB un

7、its are usually expressed in degrees. Integral is a control action that automatically eliminates droop or offset. Offset is the same as droop and is the difference in temperature between the process temperature and the set point. Droop or offset is a typical result when using proportional control. I

8、ntegral is also known as “Reset”. Is also known as “Reset”.Derivative is the rate of change in a process temperature. Large values prevent overshoot but can cause sluggishness. It is also known as “Rate”Since fuzzy controllers are nonlinear, it is more difficult to set the controller gains compared

9、to proportional-integral-derivative (PID) controllers. The idea is to start with a tuned, conventional PID controller, replace it with an equivalent linear fuzzy controller, make the fuzzy controller nonlinear, and eventually fine-tune the nonlinear fuzzy controller. This is relevant whenever a PID

10、controller is possible or already implemented. When the control problem is to regulate the process output around a set point, it is natural to consider HUURU as an input, even to a fuzzy controller, and it follows that the integral of the error and the derivative of the error may be useful inputs as

11、 well. In a fuzz field PID controller, however, it is difficult to tell the effect of each gain factor on the rise time, overshoot, and settling time, since it is most often nonlinear and has more tuning gains than a PID controller. A systematic tuning procedure would make it easier to install fuzzy

12、 controllers, and it might pave the way for auto-tuning of fuzzy controllers.PID controllers may be tuned in a variety of ways, including hand tuning, Ziegler-Nichols tuning, loop shaping, analytical methods, by optimization, pole placement, or auto-tuning (Smith, 1979; Astrom & Hagglund, 1995). Fur

13、thermore, fuzzy controllers show similarities with PID controllers under certain assumptions (Siler & Ying, 1989; Mizu-moto, 1992; Qiao & Mizumoto, 1996; Tso & Fung, 1997).But there is still a gap, it seems, between the PID tuning methods and a design strategy for fuzzy controllers of the PID type.P

14、ID design techniques, before implementing the fuzzy controller:1. Tune a PID controller2. Replace it with an equivalent linear fuzzy controller3. Make the fuzzy controller nonlinearIt seems sensible to start the controller design with a crisp PID controller, maybe even just a P controller, and get t

15、he system stabilized.If the process dynamics are considered, the closed loop system will normally be unstable if Ns are high. Obviously the setting of Ns is a balance between the control objectives: stability, noise sensitivity, and load regulation. A PID controller may be tuned using.Ziegler-Nichol

16、s.(a) Increase the proportional gain until the system oscillates; that gain is the ultimate gain.(b) Read the time between peaks at this setting.(c) The sample period may be related to the derivative gain.In connection with the Ziegler-Nichols rules, this implies that should approximately equal 4 _

17、8 percent of the ultimate period is another rule says that it should be chosen somewhat smaller than the dominating time constant in the process, Ziegler and Nichols also give another method called the UHDFWLRQ FXUYH or VWHS UHVSRQVH method (see for example Astrom & Hagglund, 1995). That method uses

18、 the open loop step response to find the gains, and this is an advantage if oscillations in the closed loop system cannot be tolerated.Action Rise time Overshoot Stability. Increase Ns faster increases get worse.Ziegler and Nichols derived the rules for a linear system with a time lag and an integra

19、tor. Their design criterion is to obtain a decay ratio of one quarter; GHFD UDWLR is the ratio between two consecutive peaks of the error after a step change in reference or load. Thus in a quarter-decay response the second overshoot is 25 % of the first， a compromise between a fast response and a s

20、mall overshoot. The results are poor for systems where the time lag is much greater than the dominating time constant. In general, the rules often result in rather poor damping, but they do provide the right magnitude of the gains.The gains found by either method, must sometimes be regarded as appro

21、ximate values, a starting point for a hand tuning. Hand tuning is based on certain rules of thumb used by experienced process engineers.The following is a hand-tuning procedure adapted from Smith (1979).(a) Remove all integral and derivative action by setting(b) Tune the proportional gain to give th

22、e desired response, ignoring any final value offset from the set point.(c) Increase the proportional gain further and adjust the derivative gain Wg to dampen the overshoot.(d) Adjust the integral gain to remove any final value offset. The tuning is a compromise between fast reaction and stability.(e

23、) Repeat until the proportional gain Ns is as large as possible.The procedure adjusts the derivative gain before the integral gain, but in practice the sequence may be reversed. The advantage of hand tuning is that a process engineer can use the procedure right away, on-line, and develop a feel for

24、how the closed loop system behaves. A disadvantage is that it may take a long time to develop this feel, and it is difficult to sense whether the final settings are optimal.&RQWURO 6XUIDFH with two inputs and one output the input-output mapping is a surface. The fuzzy PD controller may be applied wh

25、en proportional control is inadequate. The derivative term reduces overshoot, but it may be sensitive to noise as well as an abrupt change of the reference causing a GHULYDWLYH NLFN_ the usual counter-measures may over-come these problems: in the former case insert a filter, and in the latter use th

26、e derivative of the process output |q instead of the error.QFUHPHQWDO FRQWURO, if there is a sustained error in steady state, integral action is necessary. The integral action will increase the control signal if there is a small positive error, no matter how small the error is; the integral action w

27、ill decrease it if the error is negative. A controller with integral action will always return to zero in steady state. It is possible to obtain a fuzzy PI controller using error and change in error as inputs to the rule base. Experience shows, however, that it is rather difficult to write rules for

28、 the integral action. Problems with LQWHJUDWRU ZLQGXS also have to be dealt with.Since a fuzzy PID controller contains a crisp PID controller as a special case, it is true to say that it performs at least as well. It is comforting in process control systems to start 21.in the PID domain and graduall

29、y make it fuzzy.From: jjiau.dtu.dk: Date: July.2005 Author: Jan Janssen附录B 译文模糊控制PID的调节PID代表比例、积分和微分。以下各项解释描述PID，当它应用于过程温度精密的控制。一个过程量是一个区域或正在被控制区，是个被控制的区或者区别一个精确的温度。PID是一个控制方法或是有三个功能或参数的模式。比例可以抑制处理响应过程。积分为衰减改善。衰减在程序设定值和实际温度之间的温度是不同的。设定值是被需要的过程温度值。积分量是将超调量和欠过度降到最小。超调量在温度单位中是数量过程温度，设定值在过程量前是稳定的。当设定值和过

30、程温度被定义的时候是相等的，这时此过程就被达到稳态。欠过度在温度单位中是一个数量过程温度 这个温度在过程前衰减到设定值达到稳态。比例项是努力控制输出，在与设定值相比是错误的。在设定值设定之后一个控制输出之间的差别是被传递的一个信号。一个输出经常是控制加热和冷却的作用。在比例范围内是被称之为“能带”，比例也经常是在温度单位中被测量。一个温度是否为20度的一个比例带，被应用在10度的设定值时， 热输出会是50。比例值越低，增益越高。在一个电路中，增益是一个放大器的数量。比例带有时被称为增益。比例带或者PB是一个范围，在这个范围内比例控制器是活跃的。在温度中PB单元是经常被这样表示的。积分是控制活动

31、能自动消除偏差。偏差（补偿）是相同于衰减并且在过程温度和给定点中是不同的。当使用比例控制的时候，衰减或偏差是一个典型的结果。积分也可以被叫做“复置”。微分是一个变化范围在过程温度中。大的微分值可避免超调，但是能引起（变超调量过小）。它也被叫做“比率”。模糊控制是非线性的，它与比例积分微分（PID）控制器相比很难设定其控制器的增益。大体是开始调节传统的PID控制器时，用一个线性的模糊控制器来代替它，做成线性的模糊控制器，与非线性的模糊控制器相比是一样的，无论任何一个PID控制器这样做都是有可能。当此过程量在给定量的周围有规律时，它自然考虑到HUURU是一个输入甚至是一个模糊控制器，跟下面的积分的

32、偏差和微分的偏差都被使用到输入当中也是很好的。然而，在模糊控制PID控制器领域，每天一个增益的原因都很难努力达到理想的上升时间的，超调量和调节时间，至从有了PID控制器就比非线性的控制器调节好多了。一个过程调节系统安装模糊控制器后，它将可能进入自动模糊控制状态。PID控制器可以是多样调节的包括手动调节，Z-N法调节，闭环调节，分析方法，通过接地很好的完善或是自动调节(Smith, 1979; Astrom & Hagglund, 1995)。而且, 在某个假设下，模糊的控制器与 PID 控制器显示出类似但是它似乎在 PID调节方法和PID 的模糊的控制器的策略类型之间，仍然是有差距。在实现模糊

33、的控制器前的PID 设计技术：1调节一个 PID 控制器。2用一个相等的线性的模糊的控制器代替它。3用一个敏感的 PID 控制器开始控制器设计可得到好的调节，似乎易卷曲的PID,也许只是用比例控制器,就可达到稳定的系统。如果过程动力学被考虑，如果 Ns 是过高的，闭环系统将通常是不稳定的。显然 Ns 设定值与控制目的之间是平衡稳定的，噪音敏感，并且有一定规则。一个 PID 控制器可以被调节使用。Z-N法：（a）增加比例的增益直到系统震荡；那增益是最终的被获得。（b）在这个设定值在波峰之间读出值。（c）样品周期可以被联系到微分增益。在有 Z-N法规则的连接，这暗示应该近似等于另外的规则说它应该被

34、选择的最终的值为 4 -8 百分比有点小在过程的举例中的时间常数。齐格勒和尼科尔斯也给被称为 UHDFWLRQ FXUYH 或 VWHS UHVSRQVH 方法的另外的方法(例如 Astrom 与 Hagglund 所见， 1995 )。如果在闭环系统的波动不能被接受，那使用开环，并且会发现增益的反应，这就是一个优点。调节上升时间射增加 Ns 更快增加的稳定性，更慢得到更坏的增量。齐格勒和尼科尔斯用一段时间的获得一个线性的系统滞后的积分的规则。在1/4衰减响应，第二调节量是第一调节量的 25 %，折中是指明在快速响应和小的调节量之间一个值。增益能通过任何一个方法被发现,必须有时被认为是近似的价

35、值,开始的一个起点为手动调节。手动调节可能是过程工程师基于经验丰富，使用了的某个经验值。下面是从Smith到一个手动的调节适应过程（1979）：（a）通过设定值设定了所有的积分和微分项。增加比例增益给出需要的响应调节量，到最后可以忽略偏移量和设定值的差值。（b）进一步增加比例的增益和调节微分的增益可能达到衰减后的超调量。（c）最后调整积分增益到所需的偏移量。调节量是在快速性和稳定性之间的中间值。（d）重复直到比例的增益是尽可能大的。在过程量调整时，积分的增益的调节在微分增益前进行，但是在实践中顺序是可以被颠倒的。手动调节优点是一个过程工程师能立即进行联机开发闭环系统的过程量的工作。一个不利条件

36、是开发这种工作花很长时间，并且得到最后的最佳的设定值是困难的。有两个输入和一个输出的 &RQWURO 6XUIDFH 映射的输入量是表面值。此时比例环节的控制是不适当的时候，模糊的比例、微分控制器就可以被使用。微分能减少超调量，但是它可以引起通常的反措施可以克服这些问题的 GHULYDWLYH NLFN_的参考的一个突然的变化一样对噪音敏感：在以前的盒子中插入一个过滤器，并且在后者使用过程时，微分输出，而不是错误。QFUHPHQWDO FRQWURO 如果处于稳定的状态有一个固定的错误，进行积分是必要的。如果有一个小的积极的错误，不管错误怎么样小，积分项都要将进行控制信号的增加,如果误差被忽略，积分的作用将减少。一个带有积分作用的控制器一直是在位于零处达到稳定。它可能获得一个模糊的 PI 控制器的基本输入规则，在使用误差和变化的方面。然而，经验显示出,这样写出的积分的作用，可能是相当困难的，有 LQWHJUDWRU ZLQGXS 的问题也必须被处理。自从一个模糊的 PID 控制器作为一种特殊的情况，包含一个易扭曲的 PID 控制器之后，它真正至少表现的很好。它在过程控制系统正在适应PID 控制领域并逐渐地实行模糊控制。摘自： jjiau.dtu.dk 日期：2005年7月 作者：Jan Janssen