基于PID算法的小型恒温水浴锅控制系统的设计外文翻译.doc

本科生毕业设计（论文）外文资料译文（ 2012 届）论文题目基于pid算法的小型恒温水浴锅控制系统的设计pid algorithm based on small constant temperature water bath pot control system design学生姓名学号专业班级指导教师任职称杭州国际服务工程学院（信息科学与工程学院）教学部制一、外文资料翻译（译文不少于2000汉字）1所译外文资料：作者：k j astrom,t hagglund书名（或论文题目）：pid contorller出版社（或刊物名称或可获得地址）：research triangle park,notth carolina:instrument society of america出版时间（或卷期号）：1995:324-3252译成中文：pid控制器比例积分微分控制器（pid调节器）是一个控制环，广泛地应用于工业控制系统里的反馈机制。pid控制器通过调节给定值与测量值之间的偏差，给出正确的调整，从而有规律地纠正控制过程。pid控制器算法涉及到三个部分：比例，积分，微分。比例控制是对当前偏差的反应，积分控制是基于新近错误总数的反应，而微分控制则是基于错误变化率的反应。这三种控制的结合可用来调节过程系统，例如调节阀的位置，或者加热系统的电源调节。根据具体的工艺要求，通过pid控制器的参数整定，从而提供调节作用。控制器的响应可以被认为是对系统偏差的响应。注意一点的是，pid算法不一定就是系统或系统稳定性的最佳控制。一些应用可能只需要运用一到两种方法来提供适当的系统控制。这是通过把不想要的控制输出置零取得。在控制系统中存在p,pi,pd,pid调节器。pi调节器很普遍，因为微分控制对测量噪音非常敏感。积分作用的缺乏可以防止系统根据控制目标而达到它的目标值。注释：由于控制理论和应用领域的差异，很多相关变量的命名约定是常用的。控制环基础 一个关于控制环类似的例子就是保持水在理想温度，涉及到两个过程，冷、热水的混合。人可以凭触觉估测水的温度。基于此他们设计一个控制行为：用冷水龙头调整过程。重复这个过程，调节热水流直到温度处于期望的稳定值。感觉水温就是对过程值或变量的测量。期望得到的温度称为给定值。控制器的输出对象和过程的输入对象称为控制参数。测量值与给定值之间的差就是偏差值，太高、太低或正常。作为一个控制器，在确定温度给定值后，就可以粗略决定改变阀门位置多少，以及怎样改变偏差值。首次估计即是pid控制器的比例度的确定。当它几乎正确时，pid控制器的积分作用就是起着逐渐调整温度的作用。微分作用就是根据水温变得更热、更冷，以及变化速率来决定什么时候、怎样调整那些阀门。当偏差小时而做了一个大变动，相当于一个大的调整控制器，会导致超调。如果控制器反复进行大的变动并且反复越过给定值的改变，控制环将会不稳定。输出值将在期望值或一常量周围摆动，甚至破坏系统稳定性。人不会这样做，因为我们是有智慧的控制人员，可以从历史经验中学习，但pid控制器没有学习能力，必须正确的设定。为有效的控制系统选择正确的参数被称为整定控制器。如果控制器在零偏差从稳定开始，然后进一步的变化将导致其它一些影响过程的能测量、不能测量值的变化，并且作用于偏差值上。除主过程以外，其他的对扰动有影响的过程可以用来抑制扰动或实现对目标值的改变。供给水温的变化就构成了对过程的一个扰动。理论上，控制器能用来控制可测量对象，以及可以影响偏差的输出、输入标准值的所有过程参数。控制器在工业中被用来调节温度，压力，流速，化学组成，速度以及其它任何存在可测量的对象。汽车游览控制就是一个自动化的过程控制的例子。由于它们悠久的历史，简易，良好的理论基础以及简单的设置、维护要求，pid控制器被许多应用实践所采纳。2.pid控制器理论注释：这部分描述pid控制器理想平行或非相互作用的形式。关于其他形式，请看“其它的表达式和pid形式”这部分。pid控制是根据它的三个参数而命名的，三参数结合起来就形成控制参数。因此： pout，iout和dout是控制器的三个参数，下面分别予以确定。2.1比例度比例度是根据当前的错误值而做出的变动。比例度可以通过恒定的kp增加来调整，称为比例增益。比例度计算如下： pout:比例度kp：比例系数，协调参数。e:偏差=sp-pvt:时间或瞬时时间（当前的）一个高的比例增益产生于一种输出值的大的变化。如果比例增益太高，系统将变得不稳定。响应地，一个小的调整产生于一小的输出变化，而如果比例增益太低，当对系统振荡作出反映时，控制作用可能太小。缺少扰动的情况下，纯粹的比例控制不能完全解决问题，但是将保留从过程中获得的具有比例增益的功能的稳态偏差。尽管有稳态补偿，理论和工业实践都表明比例度在输出控制中起到大部分的作用。2.2积分值积分值的大小与偏差的大小及持续时间成正比。根据即时的超时的错误改正，进行积累补偿。积累的误差通过积分调节后再作用于输出。对总的控制作用的积分大小由积分时间常数来决定，即ki，积分值计算如下： iout：积分值ki:积分时间常数，协调参数e:偏差=sp-pv：积分时间积分值加速面向设定值的过程运动并且消除残余的只与控制器发生作用的稳态偏差。然而，因为积分从过去的积累误差作出反应，引起当前的值越过设定值（跨过设定值向其它方向改变）。想了解更多的关于积分和控制器稳定度的知识，请参见关于环路调谐的部分。2.3微分值过程偏差的变化率通过超时错误的斜率来计算（即它第一个关于调节的微分），并增加由微分时间常数kd引起的变化的速率。对整个控制行为的微分作用的大小称为微分值kd。微分值计算如下： dout:微分输出值kd:微分时间常数，协调参数e:偏差=sp-pvt:时间或瞬时时间（当前的）微分作用减缓了控制器输出的变化率，这种效果最接近于控制器的给定值。因此，微分控制用来降低由积分部分产生的因素并改进控制器过程控制的稳定度。但是，信号噪音对偏差值非常敏感，而且如果噪音和微分度足够大的话，将使系统变得不稳定。2.4摘要 三种参数控制的输出值，比例，积分和微分综合起来能够计算出pid调节器的输出，计算控制器输出时，pid算法的最终形式u(t)为：协调参数分别是：kp:比例增益偏差愈大时，kp也愈大，比例期补偿更大。过大的比例增益会导致系统的不稳定乃至崩溃。ki:积分，ki越大时，稳态偏差会更迅速地被消除。在达到稳态之前，在瞬态响应期间组合的任何误差必须分开。kd：微分。kd越大时，越容易超调，但是不同扰动区域的信号噪音的瞬态响应可能导致系统的不稳定。3.环路调谐如果pid控制器参数选择的不正确，控制过程输入可能是不稳定的，即：它的输出有分歧，有或没有动摇，并且只通过饱和或者机械破损是有限的。控制环的协调根据那些期望控制过程的最佳值来调整它的控制参数。最佳控制行为就是过程能根据应用作出相应的变化。一些过程不允许在设定值以外易变的过程超限，如果发生了，将是不安全的。其它过程必须在达到新设定值过程前把用掉的能量减到最小。通常，过程要求稳定，不可因为过程条件和给定值的任何变化而摆动。一些过程有一定的非线性，因此在系统满负荷下正常工作的参数在系统零负荷下将停止工作。这部分为环路调谐描述了一些传统的手工方法。pid环的调节有几种方法。最有效的方法一般与某种形式的过程模型的发展有关，然后选择的p,i和基于动态模型参数的d。手工协调方法相对来说可能没有效率。方法的选择基本依赖于控制环是否可以协调，以及系统的响应时间。如果系统可被离线工作，最好的协调方法经常与输入的阶跃变化系统有关，输出值的测量作为一个时间函数，并用来确定控制参数。3.1 手工调节如果系统必须保持在线，一种协调方法把积分和微分时间常数置零。增加p值直到环的输出值摆动，然后，p值应该大约被设为标准值的四分之一。 然后增加d直到过程补偿在足够的时间内是正确的。不过，d值太大将引起不稳定。最后，增加i值，如果需要的话，直到那些环在负荷扰动之后可迅速到达给定值。不过，i值太大将引起过度的反应并且超调。快速pid环路调谐通常越过微小扰动并且能更迅速地达到给定值；但是，一些系统不能承受超调，这时，采用超调闭环系统是有必要的，这个要求p值确定为引起系统摆动的p值的一半。3.2 zieglernichols 方法 另一种调节方式方法正式被称为 zieglernichols方法，由约翰g.齐格勒和纳撒尼尔b.尼科尔斯发明。如同在上面的方法内，i和d常数开始时先被置零。p值增加直至达到kc值，此时闭环输出值稳定。kc和pc用来象显示的那样设定目标值： 3.3 pid调节软件 现在大多数的现代工业设备自动控制环不再使用以上介绍的各种手工计算方法。相应地，pid协调和循环优化软件被用来保证结果的确定。这些软件自动收集数据，构建过程模型，并且建立最佳的调节方式。一些软件包甚至能根据参考值的变化规律来开发数据库。数学pid环路调节在系统里引起一个推动，然后根据被控制的系统的频率响应设计pid 环标准值。在有几分钟响应时间的环、数学环路调谐中被推荐，因为反复试验要花费数天，而仅仅是为了找到一套稳定的环价值。 而最佳的控制值更难以发现。一些数字环控制器提供非常小的特征值变化，被送入系统自动控制过程，使控制器本身实现最佳控制。 根据不同的性能准则环，还有其他公式对系统是可提供的。 4.对pid算法的修改基于pid算法给pid控制应用提出了一些挑战。关于理想pid实施的一个普遍问题不可缺少的终了。这可以被处理通过：初始化控制器对期望值不可缺少。整定函数，知道pv已经进入可控制的区域。限制不可缺少的偏差被计算的时间段。避免不可缺少的时间段高于或低于预设值。许多pid循环控制一个机械设备（如一个阀门）。机械维护可能是主要的费用，并在对输入信号的机械反应里以某些形式抑制扰动。机械的比率主要是一个设备变动一次的函数。pid能产生一输出值，减小系统输出的频率。如果变化缓慢，修改控制器使其输出稳定是可以的。实际输出值改变之前，被计算的输出值必须保持稳定。当系统偏差值增加时，比例、微分控制能产生积极的变动，例如设定值的变动。就微分而言，取决于对错误的积分。5.pid控制的限制当pid控制器适用于很多控制问题时，它在一些应用过程中不好使用。当单独使用并且必须降低pid环路增益时，pid控制器会给出劣质的控制性能。因此，控制系统不超调。在给定值附近摆动。控制系统可以通过结合pid控制器与前馈控制来进行改进。关于系统的知识，可以用前馈和pid输出来改进总的系统性能。单独的前馈控制经常能提供主要控制器输出值的部分。pid控制器还能对在sp和pv的实际值之间的偏差作出反应。因为前馈生产没被过程反馈影响，它永远不能引起控制系统摆动，且有助于改进系统的稳定性。例如，在大多数运动控制系统中，为了在控制一机械负荷，需要更多的来自电动机、发动机或作动器的力量或者力矩。如果一速度pid控制器被用来控制负荷的速度，并驱动被原动力使用的力或者力矩，它有利于赋予负荷所需的加速度，恰当估价并且给pid速度环控制器的输出添加给定值。这表明每当负荷被加速或者被降速时，成比例的力量从那些原动力产生而不受反馈值影响任何导致输出增加或减少的因素，为了降低给定值与反馈值的差值。同时工作时，结合的开环前馈控制器和封闭环pid控制器能提供一个更敏感、可靠的控制系统。面临pid 控制器的另一个问题是他们是在线的。 因此，在非线性系统(象空调系统那样)内的pid 控制器的工作是易变的。经常pid 控制器通过各种方法获得pid值或者模糊逻辑来进一步提高。 更进一步的实际应用问题起因于连接控制器的检测仪表。保证足够高的取样率，测量精密和测量准确度以使控制器取得足够的控制性能。 一个关于微分方面的问题是少量测量或者过程噪音能引起输出的大量改变。 为了除去高频率的噪音组成部分，用低通滤波器过滤测量数据是经常有帮助的。 不过，低通滤波器和微分控制能互相消除，那么以检测仪表方法降低噪音是更好的选择。 或者，这些不同的因素可以被很多系统避免。这相当于使用pid 控制器作为一个pi控制器。 6. 串级控制 pid 控制器的一个特别的优势是两个pid 控制器可以一同被使用以产生更好的动态特性。 这被称作串联pid 控制。 在串级控制，有二个pid控制器控制另一个参数值。 一个pid 控制器担任外环控制器，例如易流动物体或者速度控制主要物质参数。另一控制器担任内环控制器，读取外环控制器的输出， 通常控制一改变更迅速的参数。数学上可以证明，通过使用串联的pid 控制器，控制器的工作频率被增加，目标的时间常数被降低。 7. pid控制的物理实现 在早期自动化过程控制的的历史上，pid 控制器被用作一个机械设备实现。这些机械控制器经常使用一根杠杆 , 曲轴和活塞由压缩空气提供能量。这些气动控制器曾经是工业标准。电子模拟控制器可以一固态或者成管状 放大器构成，例如一个电容器和一个电感。 电子模拟pid 控制环经常在更复杂的电子系统内被发现，例如，头一个磁盘驱动器位置的确定，动力电源的限制或者甚至一台现代地震仪的运动。现在，用microcontrollers或者fpgas实现的数字控制器已经基本上替换电子控制器。 大多数现代pid 工业控制器被可编程序逻辑控制器里的软件实现或者作为数字控制器。软件实现有优势，即他们相对便宜，并且关于pid 算法的实施是灵活的。3.外文资料原文：title:pid controllerauthor: k j astrom,t hagglundpress: research triangle park,notth carolina:instrument society of americatime: 1995:324-325pid controllera proportionalintegralderivative controller (pid controller) is a generic .control loop feedback mechanism widely used in industrial control systems. a pid controller attempts to correct the error between a measured process variable and a desired setpoint by calculating and then outputting a corrective action that can adjust the process accordingly.the pid controller calculation (algorithm) involves three separate parameters; the proportional, the integral and derivative values. the proportional value determines the reaction to the current error, the integral determines the reaction based on the sum of recent errors and the derivative determines the reaction to the rate at which the error has been changing. the weightedsum of these three actions is used to adjust the process via a control element such as the position of a control valve or the power supply of a heating element.by tuning the three constants in the pid controller algorithm the pid can provide control action designed for specific process requirements. the response of the controller can be described in terms of the responsiveness of the controller to an error, the degree to which the controller overshoots the setpoint and the degree of system oscillation. note that the use of the pid algorithm for control does not guarantee optimal control of the system or system stability.some applications may require using only one or two modes to provide the appropriate system control. this is achieved by setting the gain of undesired control outputs to zero. a pid controller will be called a pi, pd, p or i controller in the absence of the respective control actions. pi controllers are particularly common, since derivative action is very sensitive to measurement noise, and the absence of an integral value may prevent the system from reaching its target value due to the control action.note: due to the diversity of the field of control theory and application, many naming conventions for the relevant variables are in common use.1.control loop basicsa familiar example of a control loop is the action taken to keep ones shower water at the ideal temperature, which typically involves the mixing of two process streams, cold and hot water. the person feels the water to estimate its temperature. based on this measurement they perform a control action: use the cold water tap to adjust the process. the person would repeat this input-output control loop, adjusting the hot water flow until the process temperature stabilized at the desired value.feeling the water temperature is taking a measurement of the process value or process variable (pv). the desired temperature is called the setpoint (sp). the output from the controller and input to the process (the tap position) is called the manipulated variable (mv). the difference between the measurement and the setpoint is the error (e), too hot or too cold and by how much.as a controller, one decides roughly how much to change the tap position (mv) after one determines the temperature (pv), and therefore the error. this first estimate is the equivalent of the proportional action of a pid controller. the integral action of a pid controller can be thought of as gradually adjusting the temperature when it is almost right. derivative action can be thought of as noticing the water temperature is getting hotter or colder, and how fast, and taking that into account when deciding how to adjust the tap.making a change that is too large when the error is small is equivalent to a high gain controller and will lead to overshoot. if the controller were to repeatedly make changes that were too large and repeatedly overshoot the target, this control loop would be termed unstable and the output would oscillate around the setpoint in either a constant, growing, or decaying sinusoid. a human would not do this because we are adaptive controllers, learning from the process history, but pid controllers do not have the ability to learn and must be set up correctly. selecting the correct gains for effective control is known as tuning the controller.if a controller starts from a stable state at zero error (pv = sp), then further changes by the controller will be in response to changes in other measured or unmeasured inputs to the process that impact on the process, and hence on the pv. variables that impact on the process other than the mv are known as disturbances and generally controllers are used to reject disturbances and/or implement setpoint changes. changes in feed water temperature constitute a disturbance to the shower process.in theory, a controller can be used to control any process which has a measurable output (pv), a known ideal value for that output (sp) and an input to the process (mv) that will affect the relevant pv. controllers are used in industry to regulate temperature, pressure, flow rate, chemical composition, speed and practically every other variable for which a measurement exists. automobile cruise control is an example of a process which utilizes automated control.due to their long history, simplicity, well grounded theory and simple setup and maintenance requirements, pid controllers are the controllers of choice for many of these applications.2.pid controller theorynote: this section describes the ideal parallel or non-interacting form of the pid controller. for other forms please see the section alternative notation and pid forms.the pid control scheme is named after its three correcting terms, whose sum constitutes the manipulated variable (mv). hence: where pout, iout, and dout are the contributions to the output from the pid controller from each of the three terms, as defined below.2.1. proportional termthe proportional term makes a change to the output that is proportional to the current error value. the proportional response can be adjusted by multiplying the error by a constant kp, called the proportional gain.the proportional term is given by: wherepout: proportional output kp: proportional gain, a tuning parameter e: error = sp pv t: time or instantaneous time (the present) change of response for varying kpa high proportional gain results in a large change in the output for a given change in the error. if the proportional gain is too high, the system can become unstable (see the section on loop tuning). in contrast, a small gain results in a small output response to a large input error, and a less responsive (or sensitive) controller. if the proportional gain is too low, the control action may be too small when responding to system disturbances.in the absence of disturbances, pure proportional control will not settle at its target value, but will retain a steady state error that is a function of the proportional gain and the process gain. despite the steady-state offset, both tuning theory and industrial practice indicate that it is the proportional term that should contribute the bulk of the output change.2.2.integral termthe contribution from the integral term is proportional to both the magnitude of the error and the duration of the error. summing the instantaneous error over time (integrating the error) gives the accumulated offset that should have been corrected previously. the accumulated error is then multiplied by the integral gain and added to the controller output. the magnitude of the contribution of the integral term to the overall control action is determined by the integral gain, ki.the integral term is given by: iout: integral output ki: integral gain, a tuning parameter e: error = sp pv : time in the past contributing to the integral response the integral term (when added to the proportional term) accelerates the movement of the process towards setpoint and eliminates the residual steady-state error that occurs with a proportional only controller. however, since the integral term is responding to accumulated errors from the past, it can cause the present value to overshoot the setpoint value (cross over the setpoint and then create a deviation in the other direction). for further notes regarding integral gain tuning and controller stability, see the section on loop tuning.2.3 derivative termthe rate of change of the process error is calculated by determining the slope of the error over time (i.e. its first derivative with respect to time) and multiplying this rate of change by the derivative gain kd. the magnitude of the contribution of the derivative term to the overall control action is termed the derivative gain, kd.the derivative term is given by: dout: derivative output kd: derivative gain, a tuning parameter e: error = sp pv t: time or instantaneous time (the present) the derivative term slows the rate of change of the controller output and this effect is most noticeable close to the controller setpoint. hence, derivative control is used to reduce the magnitude of the overshoot produced by the integral component and improve the combined controller-process stability. however, differentiation of a signal amplifies noise and thus this term in the controller is highly sensitive to noise in the error term, and can cause a process to become unstable if the noise and the derivative gain are sufficiently large.2.4 summarythe output from the three terms, the proportional, the integral and the derivative terms are summed to calculate the output of the pid controller. defining u(t) as the controller output, the final form of the pid algorithm is: and the tuning parameters arekp: proportional gain - larger kp typically means faster response since the larger the error, the larger the proportional term compensation. an excessively large proportional gain will lead to process instability and oscillation. ki: integral gain - larger ki implies steady state errors are eliminated quicker. the trade-off is larger overshoot: any negative error integrated during transient response must be integrated away by positive error before we reach steady state. kd: derivative gain - larger kd decreases overshoot, but slows down transient response and may lead to instability due to signal noise amplification in the differentiation of the error. 3. loop tuningif the pid controller parameters (the gains of the proportional, integral and derivative terms) are chosen incorrectly, the controlled process input can be unstable, i.e. its output diverges, with or without oscillation, and is limited only by saturation or mechanical breakage. tuning a control loop is the adjustment of its control parameters (gain/proportional band, integral gain/reset, derivative gain/rate) to the optimum values for the desired control response.the optimum behavior on a process change or setpoint change varies depending on the application. some processes must not allow an overshoot of the process variable beyond the setpoint if, for example, this would be unsafe. other processes must minimize the energy expended in reaching a new setpoint. generally, stability of response (the reverse of instability) is required and the process must not oscillate for any combination of process conditions and setpoints. some processes have a degree of non-linearity and so parameters that work well at full-load conditions dont work when the process is starting up from no-load. this section describes some traditional manual methods for loop tuning.there are several methods for tuning a pid loop. the most effective methods generally involve the development of some form of process model, then choosing p, i, and d based on the dynamic model parameters. manual tuning methods can be relatively inefficient.the choice of method will depend largely on whether or not the loop can be taken offline for tuning, and the response time of the system. if the system can be taken offline, the