外文翻译PID控制器.docx

1、外文翻译PID控制器PID controllerFrom Wikipedia, the free encyclopediaA 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

2、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 c

3、urrent 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 contr

4、ol 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,

5、 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 appropria

6、te 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

7、 noise, and the absence of an integral value may prevent the system from reaching its target value due to the control action.A block diagram of a PID controllerNote: Due to the diversity of the field of control theory and application, many naming conventions for the relevant variables are in common

8、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

9、 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 pr

10、ocess 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.

11、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 gradual

12、ly 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 equival

13、ent 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 decayin

14、g 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

15、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 a

16、nd 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 out

17、put (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 pro

18、cess 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-

19、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 outp

20、ut 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 proporti

21、onal 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 t

22、he 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 acti

23、on 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 tunin

24、g 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 o

25、ver 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 determin

26、ed by the integral gain, Ki.The integral term is given by: Change of response for varying KiWhere 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

27、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

28、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. i

29、ts 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: Change of response for varying KdWhere Do

30、ut: 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 r

31、educe 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 u

32、nstable 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 oscil