《PID控制器毕业论文外文翻译.doc》由会员分享，可在线阅读，更多相关《PID控制器毕业论文外文翻译.doc（17页珍藏版）》请在三一办公上搜索。
1、英文文献翻译外文资料原文：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
2、 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; th
3、e 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
4、 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. Th
5、e 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 sy
6、stem 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 contro
7、l 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 applic
8、ation, 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 fee
9、ls 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
10、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
11、 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 controll
12、er. 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
13、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
14、 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 eff
15、ective 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
16、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 proc
17、ess 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 whic
18、h 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
19、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 (
20、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
21、 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 proportio
22、nal 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 sens
23、itive) 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 proportiona
24、l 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 th
25、e 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 con
26、tribution 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 th
27、e 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 overshoo
28、t 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 slo
29、pe 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: De
30、rivative 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
31、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 unstabl
32、e 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:
33、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 ste
34、ady 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 l
35、ead 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
36、 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 pr
37、ocess 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 res
38、ponse (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. Th
39、is 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
40、 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 best tuning method often involves subjecting the system to a step change in input, measuring th
41、e output as a function of time, and using this response to determine the control parameters. Choosing a Tuning Method MethodAdvantagesDisadvantages Manual TuningNo math required. Online method.Requires experienced personnel. ZieglerNicholsProven Method. Online method.Process upset, some trial-and-er
42、ror, very aggressive tuning. Software ToolsConsistent tuning. Online or offline method. May include valve and sensor analysis. Allow simulation before downloading.Some cost and training involved. Cohen-CoonGood process models.Some math. Offline method. Only good for first-order processes.3.1 Manual
43、tuningIf the system must remain online, one tuning method is to first set the I and D values to zero. Increase the P until the output of the loop oscillates, then the P should be left set to be approximately half of that value for a quarter amplitude decay type response. Then increase D until any of
44、fset is correct in sufficient time for the process. However, too much D will cause instability. Finally, increase I, if required, until the loop is acceptably quick to reach its reference after a load disturbance. However, too much I will cause excessive response and overshoot. A fast PID loop tunin
45、g usually overshoots slightly to reach the setpoint more quickly; however, some systems cannot accept overshoot, in which case an over-damped closed-loop system is required, which will require a P setting significantly less than half that of the P setting causing oscillation.3.2 ZieglerNichols metho
46、dAnother tuning method is formally known as the ZieglerNichols method, introduced by John G. Ziegler and Nathaniel B. Nichols. As in the method above, the I and D gains are first set to zero. The P gain is increased until it reaches the critical gain Kc at which the output of the loop starts to osci
47、llate. Kc and the oscillation period Pc are used to set the gains as shown:3.3 PID tuning softwareMost modern industrial facilities no longer tune loops using the manual calculation methods shown above. Instead, PID tuning and loop optimization software are used to ensure consistent results. These s
48、oftware packages will gather the data, develop process models, and suggest optimal tuning. Some software packages can even develop tuning by gathering data from reference changes.Mathematical PID loop tuning induces an impulse in the system, and then uses the controlled systems frequency response to
49、 design the PID loop values. In loops with response times of several minutes, mathematical loop tuning is recommended, because trial and error can literally take days just to find a stable set of loop values. Optimal values are harder to find. Some digital loop controllers offer a self-tuning feature in which very small setpoint changes a
