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About This Manual
Chapter 1
System Requirements ....................................................................................................1-1
Activation Instructions...................................................................................................1-2
Chapter 2
The Precise PID Algorithm............................................................................................2-4
Error Calculation .............................................................................................2-4
Trapezoidal Integration ...................................................................................2-5
Chapter 3
Setting Timing.................................................................................................3-1
PID Controller .................................................................................................3-4
Using PID with Autotuning.............................................................................3-6
Using PID with Gain Scheduling ....................................................................3-7
Using PID with Lead-Lag ...............................................................................3-8
Using PID with Setpoint Profiling ..................................................................3-8
Using Ramp Generators ..................................................................................3-9
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Contents
Converting between Percentage of Full Scale and Engineering Units ........... 3-9
Using PID on Real-Time (RT) Targets........................................................... 3-10
Using PID with DAQ Devices........................................................................ 3-10
Appendix A
Technical Support and Professional Services
Glossary
Index
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About This Manual
The LabWindows/CVI PID Control Toolkit User Manual describes the PID
Control Toolkit for LabWindows™/CVI™. The manual describes the
features, functions, and operation of the toolkit. To use this manual, you
need a basic understanding of process control strategies and algorithms.
Conventions
The following conventions appear in this manual:
»
The » symbol leads you through nested menu items and dialog box options
to a final action. The sequence File»Page Setup»Options directs you to
pull down the File menu, select the Page Setup item, and select Options
from the last dialog box.
This icon denotes a note, which alerts you to important information.
bold
Bold text denotes items that you must select or click in the software, such
as menu items and dialog box options. Bold text also denotes parameter
names.
italic
Italic text denotes variables, emphasis, a cross-reference, or an introduction
to a key concept. Italic text also denotes text that is a placeholder for a word
or value that you must supply.
monospace
Text in this font denotes text or characters that you should enter from the
keyboard, sections of code, programming examples, and syntax examples.
This font is also used for the proper names of disk drives, paths, directories,
programs, subprograms, subroutines, device names, functions, operations,
variables, filenames, and extensions.
Related Documentation
The following documents contain information that you may find helpful as
you read this manual:
•
•
•
•
LabWindows/CVI PID Control Toolkit Help
LabWindows/CVI Help
NI-DAQmx Help
Traditional NI-DAQ (Legacy) C Function Reference Help
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1
Overview of the PID Control
Toolkit
This chapter describes how to install the toolkit and describes
Proportional-Integral-Derivative (PID) control applications.
System Requirements
Your computer must meet the following minimum system requirements to run the PID
Control Toolkit:
•
•
LabWindows/CVI 7.x or later
Windows Vista/XP/2000
Installation Instructions
If you already have an earlier version of the PID Control Toolkit installed on your computer,
you must uninstall it before installing this version of the PID Control Toolkit.
Note When you install the PID Control Toolkit, your user account must have
administrator privileges.
Complete the following steps to install the PID Control Toolkit:
1. Insert the PID Control Toolkit CD into the CD drive. If the CD does not run
automatically, open Windows Explorer, right-click the CD drive icon, and select
AutoPlay.
2. On installation startup, the National Instruments PID Control Toolkit screen appears.
Click Install Toolkit.
3. In the User Information panel, enter your name and organization and the serial number
found on your Certificate of Ownership card. LabWindows/CVI uses this serial number
when you run the NI Activation Wizard.
successfully installed, click Finish.
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Chapter 1
Overview of the PID Control Toolkit
Activation Instructions
The first time you launch LabWindows/CVI after installing the PID Control Toolkit, you are
prompted to activate the toolkit. Complete the following steps to activate the PID Control
Toolkit.
1. Click Activate Products.
2. Select the Automatically activate through a secure Internet connection option and
click Next. Your computer must be connected to the Internet for this option to work.
If you do not have Internet access on your computer, refer to the LabWindows/CVI
Release Notes.
3. Enter the serial number with the number found on your Certificate of Ownership card.
Click Next.
4. Fill in the necessary information and click Next.
5. Check the option if you would like to receive a confirmation email of your activation and
click Next.
6. After a brief moment, you should receive a message indicating whether the PID Control
Toolkit has been activated or not. Click Next.
Note If your activation was not successful, you can update the serial number, get help
from National Instruments, or evaluate the toolkit.
7. Continue to follow the instructions on the screen.
8. When you successfully activate, click Finish. LabWindows/CVI displays a window
indicating when this license expires.
For more information about activation, refer to the LabWindows/CVI Release Notes.
PID Control Toolkit Applications
The PID Control Toolkit contains functions you can use to develop LabWindows/CVI control
applications. For more information about the types of applications you can develop, refer to
the example programs that are installed with the toolkit.
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Chapter 1
Overview of the PID Control Toolkit
PID Control
Currently, the PID algorithm is the most common control algorithm used in industry. Often,
PID is used to control processes that include heating and cooling systems, fluid level
monitoring, flow control, and pressure control. When using PID control, you must specify a
process variable and a setpoint. The process variable is the system parameter you want to
control, such as temperature, pressure, or flow rate. The setpoint is the desired value for the
parameter you are controlling. A PID controller determines a controller output value, such as
the heater power or valve position. When applied to the system, the controller output value
drives the process variable toward the setpoint value.
You can use the PID Control Toolkit functions with National Instruments hardware to develop
LabWindows/CVI control applications. Use I/O hardware, such as DAQ devices, FieldPoint
I/O modules, or GPIB boards, to connect your PC to the system you want to control. You can
use the LabWindows/CVI I/O functions with the PID Control Toolkit to develop a control
application or modify the examples provided with the toolkit.
Using the PID Control Toolkit functions, you can develop the following control applications
based on PID controllers:
•
Proportional (P), proportional-integral (PI), proportional-derivative (PD), and
proportional-integral-derivative (PID) algorithms
•
•
•
•
•
•
•
•
•
Gain-scheduled PID
PID autotuning
Precise PID
Lead-lag compensation
Setpoint profile generation
Multiloop cascade control
Feedforward control
Override (minimum/maximum selector) control
Ratio/bias control
Refer to the LabWindows/CVI PID Control Toolkit Help, which you can access by selecting
Start»All Programs»National Instruments»PID Control Toolkit for CVI»LabWindows
CVI PID Help, for more information about the functions.
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2
PID Algorithms
This chapter explains the fast PID, precise PID, and autotuning algorithms.
The PID Algorithm
The PID controller compares the setpoint (SP) to the process variable (PV) to obtain the error
(e), as follows:
e = SP – PV
Then the PID controller calculates the controller action, u(t), as follows. In this equation, Kc
is the controller gain.
t
⎛
⎞
⎟
⎟
⎠
1
de
dt
⎜
----
-----
u(t) = Kc e +
edt + Td
⎜
⎝
∫
T
i
0
If the error and the controller output have the same range, –100% to 100%, controller gain is
the reciprocal of proportional band. Ti is the integral time in minutes, also called the reset
time, and Td is the derivative time in minutes, also called the rate time. The following formula
represents the proportional action.
up(t) = Kce
The following formula represents the integral action.
t
Kc
-------
uI(t) =
edt
∫
Ti
0
The following formula represents the derivative action.
de
-------
uD(t) = KcT
d dt
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Chapter 2
PID Algorithms
Implementing the PID Algorithm with the PID Functions
This section describes how the PID Control Toolkit functions implement the fast (positional)
PID algorithm. The fast PID algorithm is the default algorithm used in the PID Control
Toolkit.
Error Calculation
The following formula represents the current error used in calculating proportional, integral,
and derivative action, where PVf is the filtered process variable.
e(k) = (SP – PVf)
Proportional Action
Proportional action is the controller gain times the error, as shown in the following formula:
uP(k)=(Kc*e(k))
Trapezoidal Integration
Trapezoidal integration is used to avoid sharp changes in integral action when there is a
sudden change in the PV or SP. Use nonlinear adjustment of the integral action to counteract
overshoot. The following formula represents the trapezoidal integration action.
k
Kc
e(i) + e(i – 1)
-----
---------------------------------
Δt
uI(k)=
∑
Ti
2
i = 1
Partial Derivative Action
Because of abrupt changes in the SP, apply derivative action to only the PV, not to the error
(e), to avoid derivative kick. The following formula represents the partial derivative action.
Td
-----
uD(k) = – Kc
(PV (k)–PV (k – 1))
f
f
Δt
Controller Output
Controller output is the summation of the proportional, integral, and derivative action,
as shown in the following formula:
u(k) = uP(k) + uI(k) + uD(k)
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Chapter 2
PID Algorithms
Output Limiting
The actual controller output is limited to the range specified for control output, as follows:
if u(k) ≥ umax then u(k) = umax
and
if u(k) ≤ umin then u(k) = umin
The following formula shows the practical model of the PID controller.
t
dPVf
-----------
dt
1
Ti
----
u(t) = Kc (SP – PV) +
(SP – PV)dt – Td
∫
0
The PID functions use an integral sum correction algorithm that facilitates anti-windup and
bumpless manual-to-automatic transfers. Windup occurs at the upper limit of the controller
output, for example, 100%. When the error (e) decreases, the controller output decreases,
moving out of the windup area. The integral sum correction algorithm prevents abrupt
controller output changes when you switch from manual to automatic mode or change any
other parameters.
The default ranges for the SP, PV, and output parameters correspond to percentage values;
however, you can use actual engineering units. If you use engineering units, you must adjust
the corresponding ranges accordingly. The Ti and Td parameters are specified in minutes.
In manual mode, you can change the manual input to increase or decrease the output.
All the PID control functions are reentrant. Multiple calls from high-level functions use
separate and distinct data.
Note As a general rule, manually drive the PV until it meets or comes close to the SP
before you perform the manual-to-automatic transfer.
Gain Scheduling
Gain scheduling refers to a system in which you change controller parameters based on
measured operating conditions. For example, the scheduling variable can be the SP, the PV,
a controller output, or an external signal. For historical reasons, the term gain scheduling is
used even if other parameters, such as the derivative time or integral time parameters, change.
Gain scheduling effectively controls a system whose dynamics change with the operating
conditions.
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Chapter 2
PID Algorithms
The Precise PID Algorithm
This section describes how the PID Control Toolkit functions implement the precise PID
algorithm.
Error Calculation
The current error used in calculating integral action for the precise PID algorithm is shown in
the following formula:
SP – PV
f
------------------------
e(k) = (SP – PVf)(L+(1 – L)*
)
SPrange
where SPrange is the range of the SP and L is the linearity factor that produces a nonlinear gain
term in which the controller gain increases with the magnitude of the error. If L is 1, the
controller is linear. A value of 0.1 makes the minimum gain of the controller 10% Kc. Use of
a nonlinear gain term is referred to as a precise PID algorithm.
The error for calculating proportional action for the precise PID algorithm is shown in the
following formula:
βSP – PV
SPrange
f
----------------------------
eb(k) = (β*SP – PVf)(L+(1 – L)*
)
where β is the setpoint factor for the Two Degree of Freedom PID algorithm described in the
Proportional Action section. The formula used to calculate derivative action for the precise
PID algorithm is the same formula used to calculate derivative action for the fast PID
algorithm.
Proportional Action
In applications, SP changes are usually larger and faster than load disturbances, while load
disturbances appear as a slow departure of the controlled variable from the SP. PID tuning for
good load-disturbance responses often results in SP responses with unacceptable oscillation.
However, tuning for good SP responses often yields sluggish load-disturbance responses.
β, when set to less than one, reduces the SP response overshoot without affecting the
load-disturbance response, indicating the use of a Two Degree of Freedom PID algorithm.
β is an index of the SP response importance, from zero to one. For example, if you consider
load response the most important loop performance, set β to 0.0. Conversely, if you want the
PV to quickly follow the SP change, set β to 1.0.
uP(k)=(Kc*eb(k))
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Chapter 2
PID Algorithms
Trapezoidal Integration
Trapezoidal integration is used to avoid sharp changes in integral action when there is a
sudden change in the PV or SP. The following formula represents the trapezoidal integration
action for the precise PID algorithm. Use nonlinear adjustment of integral action to counteract
the overshoot. The larger the error, the smaller the integral action, as shown in the following
formula and in Figure 2-1.
k
Kc
e(i) + e(i – 1)
1
-----
---------------------------------
------------------------------
uI(k)=
Δt
10*e(i)2
1 + --------------------
2
∑
Ti
2
i = 1
SPrng
Figure 2-1. Nonlinear Multiple for Integral Action (SPrng = 100)
The Autotuning Algorithm
Use autotuning to improve performance. Often, many controllers are poorly tuned. As a
result, some controllers are too aggressive and some controllers are too sluggish. PID
controllers are difficult to tune when you do not know the process dynamics or disturbances.
In this case, use autotuning. Before you begin autotuning, you must establish a stable
controller, even if you cannot properly tune the controller on your own.
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Chapter 2
PID Algorithms
Figure 2-2 illustrates the autotuning procedure excited by the setpoint relay experiment,
which connects a relay and an extra feedback signal with the SP. Notice that the PID Library
autotuning functions directly implement this process. The existing controller remains in
the loop.
SP
PV
e
+
–
+
–
P(I) Controller
Process
Relay
Figure 2-2. Process under PID Control with Setpoint Relay
For most systems, the nonlinear relay characteristic generates a limiting cycle from which the
autotuning algorithm identifies the relevant information needed for PID tuning. If the existing
controller is proportional only, the autotuning algorithm identifies the ultimate gain Ku and
ultimate period Tu. If the existing model is PI or PID, the autotuning algorithm identifies the
dead time τ and time constant Tp, which are two parameters in the integral-plus-deadtime
model, as follows:
e–τs
Tps
--------
GP(s) =
Tuning Formulas
This package uses Ziegler and Nichols’ heuristic methods for determining the parameters of
a PID controller. When you autotune, select one of the following types of loop performance:
fast (1/4 damping ratio), normal (some overshoot), or slow (little overshoot). Refer to the
following tuning formula tables for each type of loop performance.
Table 2-1. Tuning Formula under P-Only Control (Fast)
Controller
Kc
Ti
Td
—
P
0.5Ku
0.4Ku
0.6Ku
—
PI
0.8Tu
0.5Tu
—
PID
0.12Tu
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Chapter 2
PID Algorithms
Table 2-2. Tuning Formula under P-Only Control (Normal)
Controller
Kc
Ti
Td
P
0.2Ku
0.18Ku
0.25Ku
—
—
—
PI
0.8Tu
0.5Tu
PID
0.12Tu
Table 2-3. Tuning Formula under P-Only Control (Slow)
Controller
Kc
Ti
Td
—
P
0.13Ku
0.13Ku
0.15Ku
—
PI
0.8Tu
0.5Tu
—
PID
0.12Tu
Table 2-4. Tuning Formula under PI or PID Control (Fast)
Controller
Kc
Tp /τ
Ti
Td
—
P
—
PI
0.9Tp /τ
3.33τ
2.0τ
—
PID
1.1Tp /τ
0.5τ
Table 2-5. Tuning Formula under PI or PID Control (Normal)
Controller
Kc
Ti
Td
—
P
0.44Tp /τ
0.4Tp /τ
0.53Tp /τ
—
PI
5.33τ
4.0τ
—
PID
0.8τ
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Chapter 2
PID Algorithms
Table 2-6. Tuning Formula under PI or PID Control (Slow)
Controller
Kc
Ti
Td
—
P
0.26Tp /τ
0.24Tp /τ
0.32Tp /τ
—
PI
5.33τ
4.0τ
—
PID
0.8τ
Note During tuning, the process remains under closed-loop PID control. It is not
necessary to switch off the existing controller and perform the experiment under open-loop
conditions. In the setpoint relay experiment, the SP signal mirrors the SP for the PID
controller.
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3
Using the PID Control Toolkit
This chapter contains the basic information you need to design a control strategy using the
PID Control Toolkit functions.
Designing a Control Strategy
When you design a control strategy, sketch a flowchart that includes the physical process and
control elements such as valves and measurements. Add feedback from the process and any
required computations. Then use the PID Control Toolkit functions to translate the flowchart
into an application.
You can handle the inputs and outputs using DAQ devices, FieldPoint I/O modules, GPIB
boards, or serial I/O ports. You can adjust polling rates in real time. Potential polling rates are
limited only by your hardware.
Setting Timing
According to control theory, a control system must sample a physical process at a rate that is
approximately 10 times faster than the fastest time constant in the physical process. For
example, a time constant of 60 s is typical for a temperature control loop in a small system.
In this case, a cycle time of 6 s is sufficient. Faster cycling offers no improvement in
performance (Corripio 1990).
The PID control feature, lead-lag feature, and setpoint profile feature in the PID Control
Toolkit are time-dependent. A component can acquire the timing information either from a
value you supply to the pidAttrDeltaTattribute or from the built-in internal timer. By
default, the pidAttrUseInternalTimerattribute is set to 1, so the component uses the
internal timer. Call PidSetAttributeand PidGetAttributeto set and get PID controller
attributes.
The internal timer calculates new timing information each time PidNextOutputis called.
When the function is called, the timer determines the time since the last call to
PidNextOutputand uses that time difference in its calculations.
attribute by setting pidAttrUseInternalTimerto 0. Use the pidAttrDeltaTattribute
for fast loops, including instances in which you use acquisition hardware to time the
controller input.
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Chapter 3
Using the PID Control Toolkit
Tuning Controllers Manually
The following controller tuning procedures are based on the work of Ziegler and Nichols, the
developers of the Quarter-Decay Ratio tuning techniques derived from a combination of
process control simulation examples to compare them. For different processes, one method
when used with online controllers cannot stand the large upsets described here. To perform
these tests, set up your control strategy with the PV, SP, and output displayed on a large strip
chart with the axes showing the values versus time. Refer to the Closed-Loop (Ultimate Gain)
Tuning Procedure and Open-Loop (Step Test) Tuning Procedure sections for more
information about disturbing the loop and determining the response from the graph. Refer to
Tuning of Industrial Control Systems as listed in Appendix A, References, for more
information about these procedures.
Closed-Loop (Ultimate Gain) Tuning Procedure
Although the closed-loop (ultimate gain) tuning procedure is very accurate, you must put your
process in steady-state oscillation and observe the PV on a strip chart. Complete the following
steps to perform the closed-loop tuning procedure.
1. Set both the derivative time and the integral time on your PID controller to 0.
2. With the controller in automatic mode, carefully increase the proportional gain (Kc) in
small increments. Make a small change in the SP to disturb the loop after each increment.
until the oscillation is sustained, neither growing nor decaying over time.
3. Record the controller proportional band (PBu) as a percent, where PBu = 100/Kc.
4. Record the period of oscillation (Tu) in minutes.
5. Multiply the measured values by the factors shown in Table 3-1 and enter the new tuning
parameters into your controller. Table 3-1 provides the proper values for a quarter-decay
ratio. If you want less overshoot, increase the gain (Kc).
Note Proportional gain (Kc) is related to proportional band (PB) as follows: Kc = 100/PB.
Table 3-1. Factors for Determining Tuning Parameter Values (Closed Loop)
Controller
PB (Percent)
2.00 PBu
Reset (Minutes)
—
Rate (Minutes)
P
—
—
PI
0.83 Tu
PID
1.67 PBu
0.50 Tu
0.125 Tu
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Open-Loop (Step Test) Tuning Procedure
The open-loop (step test) tuning procedure assumes that you can model any process as a
first-order lag and a pure deadtime. This method requires more analysis than the closed-loop
tuning procedure, but the process does not need to reach sustained oscillation. Therefore, the
open-loop tuning procedure might be quicker and more reliable for many processes. Observe
the output and the PV on a strip chart that shows time on the x-axis. Complete the following
steps to perform the open-loop tuning procedure.
1. Put the controller in manual mode, set the output to a nominal operating value, and allow
the PV to settle completely. Record the PV and output values.
2. Make a step change in the output. Record the new output value.
3. Wait for the PV to settle. From the chart, determine the values as derived from the sample
displayed in Figure 3-1. The variables represent the following values:
•
•
•
Td—Deadtime, in minutes
T—Time constant, in minutes
K—Process gain = change in PV/change in output
Figure 3-1. Output and Process Variable Strip Chart
4. Multiply the measured values by the factors shown in Table 3-2 and enter the new tuning
parameters into your controller. Table 3-2 provides the proper values for a quarter-decay
ratio. If you want less overshoot, reduce the gain (Kc).
Table 3-2. Factors for Determining Tuning Parameter Values (Open Loop)
Controller
PB (Percent)
100 (KTd/T)
110 (KTd/T)
80 (KTd/T)
Reset (Minutes)
—
Rate (Minutes)
P
—
—
PI
3.33 Td
PID
2.00 Td
0.50 Td
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Using the PID Library
The following sections describe how to use the PID Library to implement a control strategy.
PID Controller
The PID controller requires several inputs, including SP, PID gains, timer interval (in case the
internal timer is not used), PV, and output range. PID gains include proportional gain, integral
time, and derivative time. The following steps provide an overview of typical PID controller
use.
1. Provide the PID gains to PidCreateto create a PID controller. PidCreatereturns a
handle that you can use to identify the PID controller in subsequent function calls.
2. Use PidSetAttributeto set the PID controller attributes such as SP, time interval,
minimum and maximum controller output values, and so on.
3. Provide the PV to the controller in a loop and use PidNextOutputto obtain the
controller output, which is again applied on the system.
4. Once the control loop ends, call PidDiscardto discard the PID controller and free its
resources.
You can call PidSetAttributewith the pidAttrOutputMinand pidAttrOutputMax
attributes to specify the range of the controller output. The default range is –100 to 100, which
corresponds to values specified in terms of percentage of full scale. However, you can change
this range so that the controller gain relates engineering units to engineering units instead of
percentage to percentage. The PID controller coerces the controller output to the specified
range. In addition, the PID controller implements integrator anti-windup when the controller
output is saturated at the specified minimum or maximum values. Refer to Chapter 2, PID
Algorithms, for more information about anti-windup.
PID Algorithms
The PID controller can use the following types of PID algorithms to determine the controller
output.
•
•
Fast PID algorithm (pidFastPidAlgorithm)
Precise PID algorithm (pidPrecisePidAlgorithm)
Use the pidAttrAlgorithmattribute, which you can set using PidSetAttribute,
to specify the algorithm to use. pidFastPidAlgorithmis the default value.
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The fast PID algorithm is faster and simpler than the precise PID algorithm. Use the fast
algorithm in fast control loops. The precise PID algorithm uses the Two Degree of Freedom
algorithm to control the PV, which gives better results than the fast PID algorithm. The precise
PID algorithm also uses extra parameters such as Beta, Linearity, and Setpoint Range, which
you can specify using PidSetAttribute. The precise PID algorithm implements a
bumpless manual-to-automatic transfer, which ensures a smooth controller output during the
transition from manual to automatic control mode.
Control Input Filter
control application. For example, you can use a filtered PV if you are measuring process
variable values using a DAQ device. To use a filtered PV, set pidAttrUseFilteredPVto 1.
By default, this attribute is set to 0. You can use PidSetProcessVariableFilterand
PidGetProcessVariableFilterto set or get custom filters.
As discussed in the Setting Timing section, the sampling rate of the control system should be
at least 10 times faster than the fastest time constant of the physical system. Therefore, if
correctly sampled, any frequency components of the measured signal that are greater than
one-tenth of the sampling frequency are a result of noise in the measured signal. Gains in the
PID controller can amplify this noise and produce unnecessary wear on actuators and other
system components. The filtered PV uses a low-pass fifth-order Finite Impulse Response
(FIR) filter to filter out unwanted noise from input signals. The cutoff frequency of the
low-pass filter is one-tenth of the sampling frequency, regardless of the actual sampling
frequency value.
Output Rate Limiting
Sudden changes in control output are undesirable or even dangerous for many control
applications. For example, a sudden large change in the SP can cause a very large change in
controller output. Although, in theory, this large change in controller output results in fast
system response, it may also cause unnecessary wear on actuators or sudden large power
demands. In addition, the PID controller can amplify noise in the system, which results in a
constantly changing controller output.
You can use output rate limiting to avoid the problem of sudden changes in controller
output. To enable output rate limiting, set pidAttrLimitOutputRateto 1, set
pidAttrOutputRateand pidAttrInitialOutputto limit the rate of change of the
controller output, and specify the controller output value on the first iteration of the control
loop, respectively. Call PidSetAttributeand PidGetAttributeto set and get these
attributes.
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Using PID with Autotuning
You can use autotuning to improve controller performance. There are two ways in which you
can autotune a controller.
•
Wizard-Based Autotuning—You can use the PID Autotuning Wizard to tune the
parameters.
•
Classic Autotuning—You can use the functions in the Autotuning class to develop a
custom autotuning user interface.
Complete the following steps to autotune a controller. These steps explain both wizard-based
and classic autotuning.
1. Call PidCreateWithAutotuneto create the controller and obtain the PID handle that
identifies that controller in subsequent function calls. The PID Library invokes the
callback function provided to PidCreateWithAutotunewhen the following PID
events occur:
•
•
•
pidNoiseEstimateEvent—Noise estimation is complete
pidRelayCycleEvent—A setpoint relay cycle is complete
pidAutotuneEvent—Autotuning is complete
When you use wizard-based autotuning, the library invokes the callback function only
when the autotuning is complete.
2. Provide the PV to the controller in a loop and obtain the controller output, which is again
applied on the system.
3. While the PID control loop is being run, call PidAutotuneShowDialogif you want to
use wizard-based autotuning. This function launches the Autotuning Wizard. To use
classic autotuning, call the functions in the Autotuning class.
4. Once the control loop ends, call PidDiscardto discard the PID controller and release
its resources.
Distributing Applications That Use Wizard-Based Autotuning
Use the LabWindows/CVI application distribution feature to deploy applications you create
using the PID Control Toolkit. The PID Control Toolkit installs CVIPIDRuntime.msmin the
C:\Program Files\Common Files\Merge Modulesdirectory. This file installs
CVIPIDAtUI.dllin the systemdirectory. If you deploy applications that use wizard-based
autotuning, you must add this merge module to the distribution.
The version of LabWindows/CVI you are using determines how you add the merge module
to the distribution. If you are using LabWindows/CVI 7.x, create a file group in the
distribution kit, and CVIPIDRuntime.msmwill be seamlessly merged in as an MSI merge
module, instead of just being included as a file.
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If you are using LabWindows/CVI 8.0 and later, click Add Additional Module in the
Drivers & Components tab of the Edit Installer dialog box. In the Select Merge Module
dialog box, browse to and select CVIPIDRuntime.msm. For additional information about
distributing LabWindows/CVI applications, refer to the LabWindows/CVI Help.
Using PID with Gain Scheduling
Most processes are non-linear. Therefore, PID parameters that produce a desired response at
one operating point might not produce a satisfactory response at another operating point.
Using the gain scheduling feature, you can apply different sets of PID parameters for different
regions of controller operation.
The gain scheduler selects and outputs one set of PID gains from a gain schedule based on the
current gain scheduling value. The gain scheduling value input can be anything and is based
on the gain scheduling criteria that you set.
Use the pidGSAttrGainScheduleCriteriaattribute to set the gain scheduling criteria.
Call PidSetGainScheduleAttributeand PidGetGainScheduleAttributeto set and
get gain scheduling attributes. The pidGSAttrGainScheduleCriteriaattribute can take
the following values:
•
•
•
•
Setpoint
Process variable
Controller output
Gain schedule variable provided by the user through the
pidGSAttrUserGainScheduleVariableattribute
The gain schedule is a list of gain sets. A gain set consists of the following features:
•
•
•
•
Proportional gain (Kc)
Integral time (Ti)
Derivative time (Td)
Gain control value
The PID Library uses the gain set that has the smallest control value that is greater than the
value of the signal specified by the gain schedule criteria. For example, if three gain sets have
control values equal to 10, 20, and 30 and the value of the signal specified by the gain schedule
criteria is 15, then the second gain set is used.
If the value of the signal specified by the gain schedule criteria is greater than the gain
also can set pidGSAttrSelectionModeto pidManualto allow you to set the gain sets
manually. By default, the mode is automatic.
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Using PID with Lead-Lag
The lead-lag compensator uses a positional algorithm that approximates a true exponential
lead-lag. Feed forward control schemes often use this kind of algorithm as a dynamic
compensator. Using lead-lag, you can simulate inertia of motors, slow settling times in pipes,
and so on. Lead compensation stabilizes a closed loop by reacting to how fast something is
changing rather than its current state. This process speeds up the reaction. Lag compensation
stabilizes a closed loop by slowing down the reaction to the present value so that the
correction is made more slowly and does not overshoot. This process slows down the reaction.
The typical usage of the lead-lag follows:
1. Pass gains to PidLeadLagCreateto create a lead-lag compensator.
PidLeadLagCreatereturns a handle you can use to identify the lead-lag compensator
in subsequent function calls.
2. Use PidSetLeadLagAttributeand PidGetLeadLagAttributeto set and get the
lead-lag compensator attributes such as time intervals and minimum/maximum output
values. Provide the input to the compensator in a loop and use PidLeadLagNextOutput
to obtain the output. This output is either applied to the system or to the controller, based
on whether the lead-lag is being used as an input or an output filter.
3. Once the control loop ends, call PidLeadLagDiscardto discard the lead-lag
compensator and release its resources. Also call PidDiscardto discard the PID
controller.
The lead-lag compensator can be used either as an input or an output filter to the PID
controller. The default output range is –100 to 100, which corresponds to values specified in
terms of percentage of full scale. However, you can change this range so that the controller
gain relates engineering units to engineering units instead of percentage to percentage. The
lead-lag compensator coerces the controller output to the specified range.
Using PID with Setpoint Profiling
Using the setpoint profiling feature, you can generate a profile of setpoint values over time for
a “ramp and soak” type PID application. For example, you might want to ramp the setpoint
temperature of an oven control system over time and then hold, or soak, the setpoint at a
certain temperature for another period of time. Use this feature to implement any arbitrary
combination of ramp, hold, and step functions. Provide (setpoint, time) pairs to the setpoint
profile. The setpoint profile maintains the setpoint specified in each pair for the corresponding
times specified in the time array.
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You can use the setpoint profiler as follows:
1. Call PidSetpointProfileCreateto create a setpoint profile. Use a pair of time and
setpoint value arrays to specify the setpoint profile with the time values in ascending
order.
2. Use PidSetSetpointProfileAttributeto set the setpoint profile attributes.
3. Use PidSetpointProfileNextSetpointto obtain the setpoint from the profile in a
loop and provide this setpoint to the controller.
4. Once the control loop ends, call PidSetpointProfileDiscardto discard the setpoint
profile and release its resources. Also call PidDiscardto discard the PID controller.
At any time in the control loop, you can use pidSetpointProfileAttrElapsedTime
to get the elapsed time. You also can check if the profile is complete using the
pidSetpointProfileAttrProfileCompleteattribute.
Using Ramp Generators
A ramp generator is a simple component that you can use to generate a ramp output. Typically,
you use a ramp generator as follows:
1. Call PidRampCreateto create a ramp generator. Specify the SP, initial output, and the
rate at which the output of the ramp changes.
2. Call PidSetRampAttributeto set the ramp generator attributes.
3. Use PidRampNextOutputto obtain the output of the ramp in a loop.
4. Call PidRampDiscardto discard the ramp generator and release its resources.
Converting between Percentage of Full Scale and Engineering Units
As described in the previous sections, the default SP, PV, and output ranges for the PID
Library functions correspond to a percentage of the full scale. Proportional gain (Kc) relates
percentage of full-scale output to percentage of full-scale input. This is the default behavior
of many PID controllers used for process control applications. To implement PID in
this way, you must scale all inputs to percentage of full scale and all controller
outputs to actual engineering units such as volts for analog output. You can use
PidConvertEGUToPercentageto convert any input from real engineering units to
percentage of full scale and PidConvertPercentageToEGUto convert the controller output
from percentage to real engineering units. PidConvertPercentageToEGUhas an
additional input parameter, bCoerce. The default value of bCoerce is TRUE, which indicates
that the output is coerced to the range.
information to convert values to percentages in the PID algorithm. The controller gain
relates the output in engineering units to the input in engineering units. For example, a gain
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value of 1 produces an output of 10 for a difference between the SP and PV of 10,
regardless of the output range and setpoint range.
Using PID on Real-Time (RT) Targets
Some PID applications are deterministic and, therefore, cannot be run on desktop operating
systems. Because the PID Library is supported on real-time (RT) targets, you can use it to
develop deterministic applications. For more information about developing and running
RT applications, refer to the LabWindows/CVI Real-Time Module Help.
Because RT systems do not support user interfaces, you cannot use wizard-based autotuning
in PID applications that are targeted for RT platforms. Any applications that are targeted for
RT must not include the following functions:
• PidAutotuneShowDialog
• PidAutotuneCloseDialog
However, you can use other functions in the Autotuning class to tune the PID controller in
applications targeted for RT platforms.
Using PID with DAQ Devices
This section addresses several important issues you might encounter when you use the DAQ
APIs to control actual processes.
Complete the following steps to use the PID Library with a DAQ device.
1. Configure the DAQ device and channels for both input and output. Also configure the
sample clocks, if necessary.
2. Call PidCreateto create the PID controller. Then call PidSetAttributeto configure
the controller attributes.
3. Within the control loop, complete the following steps:
a. Read the input from the DAQ device.
b. Modify/manipulate the input so that it can be provided to the controller. This step is
optional and required only in cases in which the controller input is derived from the
acquired DAQ input.
c. Supply this input to the controller and obtain the controller output.
d. Modify/manipulate the controller output so that it can be used as the DAQ device
output. This step is optional and required only in cases in which the output on the
DAQ device is derived from the controller output.
4. Discard the controller to release its resources. Also clear all the DAQ tasks. In some
cases, just before clearing the DAQ tasks, the DAQ device outputs a 0.
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The control loops can be timed in the following ways:
•
Software-Timed—In software-timed control loops, the timing is controlled by the
software loop rate. You can implement a software loop with a timer construct, such as a
timer control or asynchronous timer, or a while loop with a delay or sleep operation at
the end.
•
Hardware-Timed—In hardware-timed control loops, the timing is controlled by the
DAQ device. The DAQ device is configured with the appropriate sample rate, and the
sample mode is set to hardware-timed.
For more information about DAQ, refer to the NI-DAQmx Help or Traditional NI-DAQ
(Legacy) C Function Reference Help, depending on the DAQ API you are using. Also refer
to the DAQ example programs.
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A
References
The Instrument Society of America (ISA), the organization that sets standards for process
control instrumentation in the United States, offers a catalog of books, journals, and training
materials to teach you the basics of process control programming.
The Corripio (1990) publication is an ISA Independent Learning Module book. It is organized
as a self-study program covering measurement and control techniques, selection of
controllers, and advanced control techniques. This book provides detailed tuning procedures.
The following material is referenced in this manual:
Corripio, A. B. 1990. Tuning of Industrial Control Systems. Raleigh, North Carolina: ISA.
Ziegler, J. G. and N. B. Nichols. 1942. “Optimum Settings for Automatic Controllers.”
Trans. ASME 64:759–68.
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B
Technical Support and
Professional Services
Visit the following sections of the award-winning National Instruments
Web site at ni.comfor technical support and professional services:
•
Support—Technical support resources at ni.com/supportinclude
the following:
–
Self-Help Technical Resources—For answers and solutions,
visit ni.com/supportfor software drivers and updates, a
searchable KnowledgeBase, product manuals, step-by-step
troubleshooting wizards, thousands of example programs,
tutorials, application notes, instrument drivers, and so on.
Registered users also receive access to the NI Discussion Forums
at ni.com/forums. NI Applications Engineers make sure every
question submitted online receives an answer.
–
Standard Service Program Membership—This program
entitles members to direct access to NI Applications Engineers
via phone and email for one-to-one technical support as well as
exclusive access to on demand training modules via the Services
Resource Center. NI offers complementary membership for a full
year after purchase, after which you may renew to continue your
benefits.
For information about other technical support options in your
area, visit ni.com/services, or contact your local office at
ni.com/contact.
•
•
Training and Certification—Visit ni.com/trainingfor
self-paced training, eLearning virtual classrooms, interactive CDs,
and Certification program information. You also can register for
instructor-led, hands-on courses at locations around the world.
System Integration—If you have time constraints, limited in-house
technical resources, or other project challenges, National Instruments
Alliance Partner members can help. To learn more, call your local
NI office or visit ni.com/alliance.
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Appendix B
Technical Support and Professional Services
If you searched ni.comand could not find the answers you need, contact
your local office or NI corporate headquarters. Phone numbers for our
worldwide offices are listed at the front of this manual. You also can visit
the Worldwide Offices section of ni.com/niglobalto access the branch
office Web sites, which provide up-to-date contact information, support
phone numbers, email addresses, and current events.
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Glossary
A
algorithm
A prescribed set of well-defined rules or processes for the solution of a
problem in a finite number of steps.
autotuning
Automatically testing a process under control to determine the controller
gains that will provide the best controller performance.
Autotuning Wizard
An automated graphical user interface provided in the Autotuning
functions. The Autotuning Wizard gathers some information about the
desired control from the user and then steps through the PID autotuning
process. You must specify to use wizard-based autotuning in
PidCreateWithAutotuneto use this feature.
B
bias
The offset added to a controller’s output.
bumpless transfer
A process in which the next output always increments from the current
output, regardless of the current controller output value. Therefore, transfer
from automatic to manual control is always bumpless.
C
cascade control
Control in which the output of one controller is the setpoint for another
controller.
closed loop
controller
A signal path that includes a forward path, a feedback path, and a summing
point and that forms a closed circuit. Also called a feedback loop.
Hardware and/or software used to maintain parameters of a physical
process at desired values.
controller output
cycle time
A quantity or condition that is varied as a function of the actuating error
signal so as to change the value of the directly controlled variable.
The time between samples in a discrete digital control system.
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Glossary
D
damping
The progressive reduction or suppression of oscillation in a device or
system.
dead time (Td)
The interval of time, expressed in minutes, between initiation of an input
change or stimulus and the start of the resulting observable response.
derivative (control)
action
Control response to the time rate of change of a variable.
E
EGU
Engineering units.
F
feedback control
Control in which a measured variable is compared to its desired value to
produce an actuating error signal that is acted upon in such a way as to
reduce the magnitude of the error.
feedback loop
See closed loop.
G
gain
For a linear system or element, the ratio of the magnitude, amplitude, or a
steady-state sinusoidal output relative to the causal input; the length of a
phasor from the origin to a point of the transfer locus in a complex plane.
gain scheduling
The process of applying different controller gains for different regions of
operation of a controller. Gain scheduling is most often used in controlling
nonlinear physical processes.
H
Hz
Hertz. Cycles per second.
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Glossary
I
Instrument Society of
America (ISA)
The organization that sets standards for process control instrumentation in
the United States.
integral (control) action Control action in which the output is proportional to the time integral of the
input. That is, the rate of change of output is proportional to the input.
K
K
Process gain.
Kc
Controller gain.
L
lag
A lowpass filter or integrating response with respect to time.
linearity factor
A value, ranging from 0 to 1, used to specify the linearity of a calculation.
A value of 1 indicates a linear operation. A value of 0 indicates a squared
nonlinear operation.
load disturbance
The ability of a controller to compensate for changes in physical
parameters of a controlled process while the setpoint value remains
constant.
M
ms
Milliseconds.
N
noise
In process instrumentation, an unwanted component of a signal or variable.
Noise may be expressed in units of the output or in percent of output span.
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Glossary
O
output limiting
Preventing a controller’s output from traveling beyond a desired maximum
range.
overshoot
The maximum excursion beyond the final steady-state value of output as
the result of an input change.
P
P
Proportional.
PD
Proportional, derivative.
Proportional, integral.
Proportional, integral, derivative.
PI
PID
PID control
A common control strategy in which a process variable is measured and
compared to a desired setpoint to determine an error signal. A proportional
gain (P) is applied to the error signal, an integral gain (I) is applied to the
integral of the error signal, and a derivative gain (D) is applied to the
derivative of the error signal. The controller output is a linear combination
of the three resulting values.
PID controller
A controller that produces proportional plus integral (reset) plus derivative
(rate) control action.
process gain (K)
process variable (PV)
For a linear process, the ratio of the magnitudes of the measured process
response to that of the manipulated variable.
The measured variable (such as pressure or temperature) in a process to be
controlled.
proportional action
Control response in which the output is proportional to the input.
proportional band (PB)
The change in input required to produce a full range change in output due
to proportional control action. PB = 100/Kc.
PSI
Pounds per square inch.
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Glossary
R
ramp
The total (transient plus steady-state) time response resulting from a sudden
increase in the rate of change from zero to some finite value of input
stimulus.
reentrant execution
Mode in which calls to multiple instances of a function can execute in
parallel with distinct and separate data storage.
S
s
Seconds.
setpoint (SP)
An input variable that sets the desired value of the controlled process
variable.
span
The algebraic difference between the upper and lower range values.
T
time constant (T)
In process instrumentation, the value T (in minutes) in an exponential
response term, A exp (–t/T), or in one of the transform factors, such as
1+sT.
trapezoidal integration
A numerical integration in which the current value and the previous value
are used to calculate the addition of the current value to the integral value.
V
V
Volts.
W
windup area
The time during which the controller output is saturated at the maximum or
minimum value. The integral action of a simple PID controller continues to
increase (wind up) while the controller is in the windup area.
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Index
A
applications, 1-3
autotuning, 3-6
classic, 3-6
DAQ devices, using PID with, 3-10
derivative action, 2-1
derivative time, 2-1
procedure, 3-6
wizard based, 3-6
diagnostic tools (NI resources), B-1
distributing applications, 3-6
documentation
distributing applications, 3-6
autotuning algorithm
tuning formulas, 2-6
conventions used in manual, vii
NI resources, B-1
PI control (fast), 2-7
drivers (NI resources), B-1
PI control (normal), 2-7
P-only control (normal), 2-7
P-only control (slow), 2-7
E
engineering units, converting from percentage
B
examples (NI resources), B-1
F
C
fast PID algorithm, 3-4
calculating controller action, 2-1
classic autotuning, 3-6
closed-loop tuning procedure, 3-2
control strategy, designing, 3-1
controller
gain scheduling, 2-3, 3-7
action, 2-1
gain, 2-1
output, 2-2
PID, 1-3
H
help file, accessing, 1-3
help, technical support, B-1
conventions used in the manual, vii
Corripio, A.B., A-1
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Index
action, 2-2
output limiting, 2-3
I
installation instructions, 1-1
instrument drivers (NI resources), B-1
integral action, 2-1
partial derivative action, 2-2
proportional action, 2-2
trapezoidal integration, 2-2
gain scheduling, 2-3
integral time, 2-1
PID controller, 1-3
typical use, 3-4
PID Library, 3-4
K
KnowledgeBase, B-1
pidAttrAlgorithm, 3-4
L
pidAttrLimitOutputRate, 3-5
pidAttrUseInternalTimer, 3-1
action, 2-5
lag compensation, 3-8
N
proportional action, 2-4
trapezoidal integration, 2-5
error calculation, 2-4
National Instruments support and
services, B-1
Nichols, N.B., A-1
nonlinear adjustment of integral
action, 2-2, 2-5
proportional action, 2-4
trapezoidal integration, 2-5
process variable, 1-3
programming examples (NI resources), B-1
proportional action, 2-2
O
open-loop tuning procedure, 3-3
output limiting, 2-3
output rate limiting, 3-5
rate time, 2-1
P
real-time targets, using PID on, 3-10
reset time, 2-1
partial derivative action, 2-2
percentage of full scale, converting from
engineering units, 3-9
PID algorithm, 2-1 to 2-3
autotuning algorithm, 2-5 to 2-8
calculating controller action, 2-1
error calculation, 2-2
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Index
S
setpoint, 1-3
ultimate gain tuning procedure, 3-2
relay experiment, 2-6
software (NI resources), B-1
step test tuning procedure, 3-3
support, technical, B-1
W
Web resources, B-1
windup, 2-3
wizard-based autotuning, 3-6
distributing applications, 3-6
T
technical support, B-1
timing information, acquiring, 3-1
timing, setting, 3-1
Z
Ziegler, J.G., A-1
training and certification (NI resources), B-1
trapezoidal integration, 2-2, 2-5
troubleshooting (NI resources), B-1
tuning procedure
closed loop, 3-2
open loop, 3-3
step test, 3-3
ultimate gain, 3-2
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