ClosedLoop PID Autotuner
Automatically tune PID gains based on plant frequency responses estimated from closedloop experiment in real time
 Library:
Simulink Control Design
Description
The ClosedLoop PID Autotuner block lets you tune a PID controller in real time against a physical plant for which you have an initial PID controller that yields a stable loop. The plant remains under closedloop control of the initial PID controller during the entire autotuning process. The block can tune the PID controller to achieve a specified bandwidth and phase margin without a parametric plant model. If you have a codegeneration product such as Simulink^{®} Coder™, you can generate code that implements the tuning algorithm on hardware, letting you tune in real time with or without using Simulink to manage the autotuning process.
If you have a plant modeled in Simulink and an initial PID controller, you can perform closedloop PID autotuning against the modeled plant. Doing so lets you preview plant response and adjust the settings for PID autotuning before tuning the controller in real time.
To achieve modelfree tuning, the ClosedLoop PID Autotuner block:
Injects a test signal into the plant to collect plant inputoutput data and estimate frequency response in real time. The test signal is combination of sinusoidal perturbation signals added on top of the plant input.
At the end of the experiment, tunes PID controller parameters based on estimated plant frequency responses near the target bandwidth.
Updates a PID Controller block or a custom PID controller with the tuned parameters, allowing you to validate closedloop performance in real time.
Unlike with the OpenLoop PID Autotuner block, the loop remains closed throughout the experiment. Keeping the loop closed helps to maintain safe operation of the plant during the estimation experiment.
You can use the ClosedLoop PID Autotuner block to tune PID controllers for:
Any stable plant
Any continuoustime plant with one or more integrators (poles at s = 0) or one or more pairs of complex poles on the imaginary axis
Any discretetime plant with one or more integrators (poles at z = –1) or pairs of complex poles on the unit circle z = 1
If you do not have an initial PID controller, you can use the OpenLoop PID Autotuner block to obtain one. You can then switch to closedloop PID autotuning for refinement or retuning.
The block supports code generation with Simulink Coder, Embedded Coder^{®}, and Simulink PLC Coder™. It does not support code generation with HDL Coder™.
For more information about using the ClosedLoop PID Autotuner block, see:
For more general information about PID autotuning and a comparison of the closedloop and openloop approaches, see When to Use PID Autotuning.
Ports
Input
u
— Signal from controller
scalar
Insert the block into your system such that this port accepts a control signal from a source. Typically, this port accepts the signal from the PID controller in your system.
Data Types: single
 double
y
— Plant output
scalar
Connect this port to the plant output.
Data Types: single
 double
start/stop
— Start and stop the autotuning experiment
scalar
To start and stop the autotuning process, provide a signal at the
start/stop
port. When the value of the signal
changes from:
Negative or zero to positive, the experiment starts
Positive to negative or zero, the experiment stops
When the experiment is not running, the block passes signals unchanged from u to u+Δu. In this state, the block has no impact on plant or controller behavior.
Typically, you can use a signal that changes from 0 to 1 to start the experiment, and from 1 to 0 to stop it. Some points to consider when configuring the start/stop signal include:
Start the experiment when the plant is at the desired equilibrium operating point. Use the initial controller to drive the plant to the operating point. If you have no initial controller (openloop tuning only) you can use a source block connected to u to drive the plant to the operating point.
Avoid any load disturbance to the plant during the experiment. Load disturbance can distort the plant output and reduce the accuracy of the frequencyresponse estimation.
Let the experiment run long enough for the algorithm to collect sufficient data for a good estimate at all frequencies it probes. There are two ways to determine when to stop the experiment:
Determine the experiment duration in advance. A conservative estimate for the experiment duration is 200/ω_{c} in superposition experiment mode or 550/ω_{c} in sinestream experiment mode, where ω_{c} is your target bandwidth.
Observe the signal at the
% conv
output, and stop the experiment when the signal stabilizes near 100%.
When you stop the experiment, the block computes tuned PID gains and updates the signal at the
pid gains
port.
You can configure any logic appropriate for your application to control the start and stop times of the experiment.
Data Types: single
 double
bandwidth
— Target bandwidth for tuning
scalar
Supply a value for the Target bandwidth (rad/sec)
parameter. See that parameter for details.
Dependencies
To enable this port, in the Tuning tab, next to Target bandwidth (rad/sec)
, select Use external source.
Data Types: single
 double
target PM
— Target phase margin for tuning
scalar
Supply a value for the Target phase margin (degrees)
parameter. See that parameter for details.
Dependencies
To enable this port, in the Tuning tab, next to Target phase margin (degrees)
, select Use external source.
Data Types: single
 double
sine Amp
— Amplitudes of injected sinusoidal signals
scalar  vector
Supply a value for the Sine Amplitudes
parameter. See that parameter for details.
Dependencies
To enable this port, in the Experiment tab, next to Sine Amplitudes
, select Use external source.
Data Types: single
 double
Output
u+Δu
— Signal for plant input
scalar
Insert the block into your system such that this port feeds the input signal to your plant.
When the experiment is running (
start/stop
positive), the block injects test signals into the plant at this port. If you have any saturation or rate limit protecting the plant, feed the signal from u+Δu into it.When the experiment is not running (
start/stop
zero or negative), the block passes signals unchanged from u to u+Δu.
Dependencies
To enable this port, in Output Signal Configuration, select control + perturbation.
Data Types: single
 double
Δu
— Plant input perturbation
scalar
The block generates a perturbation signal at this port. Typically, you inject the perturbation from this port via a sum block, as shown in the following diagram.
When the experiment is running (start/stop positive), the block generates perturbation signals at this port.
When the experiment is not running (start/stop zero or negative), the signal at this port is zero. In this state, the block has no effect on the plant.
Dependencies
To enable this port, in Output Signal Configuration, select perturbation only.
Data Types: single
 double
% conv
— Convergence of FRD estimation during experiment
scalar
When the experiment is running (start/stop
positive), the block
injects test signals into the plant and measures the plant response at
y
. It uses these signals to estimate the frequency response of
the plant at several frequencies around the target bandwidth for tuning. %
conv
indicates how close to completion the estimation of the plant
frequency response is. Typically, this value quickly rises to about 90% after the
experiment begins, and then gradually converges to a higher value. Stop the experiment
when it levels off near 100%.
Data Types: single
 double
pid gains
— Tuned PID coefficients
bus
This 4element bus signal contains the tuned PID gains P,
I, D, and the filter coefficient
N. These values correspond to the P
,
I
, D
, and N
parameters in
the expressions given in the Form
parameter. Initially, the values
are 0, 0, 0, and 100, respectively. The block updates the values when the experiment
ends. This bus signal always has four elements, even if you are not tuning a PIDF
controller.
If you have a PID controller associated with the block, you can update that controller with these values after the experiment ends. To do so, in the Block tab, click Update PID Block.
Data Types: single
 double
estimated PM
— Estimated phase margin with tuned controller
scalar
This port outputs the estimated phase margin achieved by the tuned controller, in degrees. The block updates this value when the tuning experiment ends. The estimated phase margin is calculated from the angle of G(jω_{c})C(jω_{c}), where G is the estimated plant, C is the tuned controller, and ω_{c} is the crossover frequency (bandwidth). The estimated phase margin might differ from the target phase margin specified by the Target phase margin (degrees)
parameter. It is an indicator of the robustness and stability achieved by the tuned system.
Typically, the estimated phase margin is near the target phase margin. In general, the larger the value, the more robust is the tuned system, and the less overshoot there is.
A negative phase margin indicates that the closedloop system might be unstable.
Dependencies
To enable this port, in the Tuning tab, select Output estimated phase margin achieved by tuned controller.
frd
— Estimated frequency response
vector
This port outputs the frequencyresponse data estimated by the
experiment. Initially, the value at frd
is [0, 0,
0, 0, 0]. During the experiment, the block injects signals at
frequencies [1/10, 1/3, 1, 3,
10]ω_{c}, where ω_{c} is
the target bandwidth. At each sample time during the experiment, the
block updates frd
with a vector containing the
complex frequency response at each of these frequencies, respectively.
You can use the progress of the response as an alternative to
% conv
to examine the convergence of the
estimation. When the experiment stops, the block updates
frd
with the final estimated frequency response
used for computing the PID gains.
Dependencies
To enable this port, in the Experiment tab, select Plant frequency responses near bandwidth.
nominal
— Plant input and output at nominal operating point
vector
This port outputs a vector containing the plant input (u+Δu) and plant output (y) when the experiment begins. These values are the plant input and output at the nominal operating point at which the block performs the experiment.
Dependencies
To enable this port, in the Experiment tab, select Plant nominal input and output.
Parameters
Type
— PID controller actions
PI
(default)  PID
 PIDF
 ...
Specify the type of the PID controller in your system. The controller type indicates what actions are present in the controller. The following controller types are available for PID autotuning:
P
— Proportional onlyI
— Integral onlyPI
— Proportional and integralPD
— Proportional and derivativePDF
— Proportional and derivative with derivative filterPID
— Proportional, integral, and derivativePIDF
— Proportional, integral, and derivative with derivative filter
When you update a PID Controller block or custom PID controller with tuned parameter values, make sure the controller type matches.
Tunable: Yes
Programmatic Use
Block Parameter: PIDType 
Type: character vector 
Values: 'P'  'I'  'PI'  'PD'  'PDF'  'PID'  'PIDF' 
Default: 'PI' 
Form
— PID controller form
Parallel
(default)  Ideal
Specify the controller form. The controller form determines the interpretation of the PID coefficients P, I, D, and N.
Parallel
— InParallel
form, the transfer function of a discretetime PIDF controller is:$$C=P+I{F}_{i}\left(z\right)+D\left[\frac{N}{1+N{F}_{d}\left(z\right)}\right],$$
where F_{i}(z) and F_{d}(z) are the integrator and filter formulas (see
Integrator method
andFilter method
). The transfer function of a continuoustime parallelform PIDF controller is:$$C=P+I\left(\frac{1}{s}\right)+D\left(\frac{Ns}{s+N}\right).$$
Other controller actions amount to setting P, I, or D to zero.
Ideal
— InIdeal
form, the transfer function of a discretetime PIDF controller is:$$C=P\left[1+I{F}_{i}\left(z\right)+D\left(\frac{N}{1+N{F}_{d}\left(z\right)}\right)\right].$$
The transfer function of a continuoustime idealform PIDF controller is:
$$C=P\left[1+I\left(\frac{1}{s}\right)+D\left(\frac{Ns}{s+N}\right)\right].$$
Other controller actions amount to setting D to zero or setting, I to
Inf
. (In ideal form, the controller must have proportional action.)
When you update a PID Controller block or custom PID controller with tuned parameter values, make sure the controller form matches.
Tunable: Yes
Programmatic Use
Block Parameter: PIDForm 
Type: character vector 
Values: 'Parallel'  'Ideal' 
Default: 'Parallel' 
Time Domain
— PID controller time domain
discretetime (default)  continuoustime
Specify whether your PID controller is a discretetime or continuoustime controller.
For discrete time, you must specify the sample time of your PID controller using the Controller sample time (sec) parameter.
For continuous time, you must also specify a sample time for the PID autotuning experiment using the Experiment sample time (sec) parameter.
Programmatic Use
Block Parameter:
TimeDomain 
Type: character vector 
Values:
'discretetime' 
'continuoustime' 
Default:
'discretetime' 
Controller sample time (sec)
— Sample time of PID controller
0.1 (default)  positive scalar  –1
Specify the sample time of your PID controller in seconds. This value also sets the sample time for the experiment performed by the block.
To perform PID tuning, the block measures frequencyresponse information up to a frequency of 10 times the target bandwidth. To ensure that this frequency is less than the Nyquist frequency, the target bandwidth, ω_{c}, must satisfy ω_{c}T_{s} ≤ 0.3, where T_{s} ω_{c} is the controller sample time that you specify with the Controller sample time (sec) parameter.
When you update a PID Controller block or custom PID controller with tuned parameter values, make sure the controller sample time matches.
Tips
If you want to run the deployed block with different sample times in your application, set this parameter to –1 and put the block in a Triggered Subsystem. Then, trigger the subsystem at the desired sample time. If you do not plan to change the sample time after deployment, specify a fixed and finite sample time.
Dependencies
To enable this parameter, set Time Domain to
discretetime
.
Programmatic Use
Block Parameter:
DiscreteTs 
Type: scalar 
Value positive scalar  –1 
Default: 0.1 
Tune at different sample time
— Enable tuning at different sample time from PID controller and experiment
off
(default)  on
Enable this parameter to run tuning at a sample rate that is different from the sample rate of the PID controller you are tuning and the frequency response estimation experiment performed by the block. The PID gain tuning algorithm is computationally intensive, and when you want to deploy the block to hardware and tune a controller with a fast sample time, some hardware might not complete the PID gain calculation in a single time step. To reduce the hardware throughput requirements, specify a tuning sample time slower than the controller sample time using the Tuning sample time (sec) parameter.
Dependencies
To enable this parameter, set Time Domain to
discretetime
.
Programmatic Use
Block Parameter:
UseTuningTs 
Type: character vector 
Value
'off'  'on' 
Default:
'off' 
Tuning sample time (sec)
— Sample time of tuning algorithm
0.2 (default)  positive scalar
Specify the sample time of the tuning algorithm in seconds.
If you intend to deploy the block on hardware with limited processing power and want to tune a controller with a fast sample time, specify a sample time such that the tuning algorithm runs at a slower rate than the PID controller you are tuning.
Dependencies
To enable this parameter, set Time Domain to
discretetime
and select Tune at
different sample time.
Programmatic Use
Block Parameter:
TuningTs 
Type: scalar 
Value positive scalar 
Default: 0.2 
Experiment sample time (sec)
— Sample time for experiment
0.02 (default)  positive scalar
Even when you tune a continuoustime controller, you must specify a sample time for the experiment performed by the block. In general, continuoustime controller tuning is not recommended for PID autotuning against a physical plant. If you want to tune in continuous time against a Simulink model of the plant, use a fast experiment sample time, such as 0.02/ω_{c}.
Dependencies
This parameter is enabled when the Time Domain is
continuoustime
.
Programmatic Use
Block Parameter:
ContinuousTs 
Type: positive scalar 
Default: 0.02 
Integrator method
— Discrete integration formula for integrator term
Forward Euler
(default)  Backward Euler
 Trapezoidal
Specify the discrete integration formula for the integrator term in your controller. In discrete time, the PID controller transfer function assumed by the block is:
$$C=P+I{F}_{i}\left(z\right)+D\left[\frac{N}{1+N{F}_{d}\left(z\right)}\right],$$
in parallel form, or in ideal form,
$$C=P\left[1+I{F}_{i}\left(z\right)+D\left(\frac{N}{1+N{F}_{d}\left(z\right)}\right)\right].$$
For a controller sample time T_{s}, the
Integrator method
parameter determines the formula
F_{i} as follows:
Integrator method  F_{i} 

Forward Euler 
$$\frac{{T}_{s}}{z1}$$ 
Backward Euler 
$$\frac{{T}_{s}z}{z1}$$ 
Trapezoidal 
$$\frac{{T}_{s}}{2}\frac{z+1}{z1}$$ 
For more information about the relative advantages of each method, see the Discrete PID Controller block reference page.
When you update a PID Controller block or custom PID controller with tuned parameter values, make sure the integrator method matches.
Tunable: Yes
Dependencies
This parameter is enabled when the Time Domain is discretetime
and the controller includes integral action.
Programmatic Use
Block Parameter: IntegratorFormula 
Type: character vector 
Values: 'Forward Euler'  'Backward Euler'  'Trapezoidal' 
Default: 'Forward Euler' 
Filter method
— Discrete integration formula for derivative filter term
Forward Euler
(default)  Backward Euler
 Trapezoidal
Specify the discrete integration formula for the derivative filter term in your controller. In discrete time, the PID controller transfer function assumed by the block is:
$$C=P+I{F}_{i}\left(z\right)+D\left[\frac{N}{1+N{F}_{d}\left(z\right)}\right],$$
in parallel form, or in ideal form,
$$C=P\left[1+I{F}_{i}\left(z\right)+D\left(\frac{N}{1+N{F}_{d}\left(z\right)}\right)\right].$$
For a controller sample time T_{s}, the
Filter method
parameter determines the formula
F_{d} as follows:
Filter method  F_{d} 

Forward Euler 
$$\frac{{T}_{s}}{z1}$$ 
Backward Euler 
$$\frac{{T}_{s}z}{z1}$$ 
Trapezoidal 
$$\frac{{T}_{s}}{2}\frac{z+1}{z1}$$ 
For more information about the relative advantages of each method, see the Discrete PID Controller block reference page.
When you update a PID Controller block or custom PID controller with tuned parameter values, make sure the filter method matches.
Tunable: Yes
Dependencies
This parameter is enabled when the Time Domain is
discretetime
and the controller includes a derivative filter
term.
Programmatic Use
Block Parameter: FilterFormula 
Type: character vector 
Values: 'Forward Euler'  'Backward Euler'  'Trapezoidal' 
Default: 'Forward Euler' 
Target bandwidth (rad/sec)
— Target crossover frequency of tuned response
1 (default)  positive scalar
The target bandwidth, specified in rad/sec, is the target value for the 0dB gain crossover frequency of the tuned openloop response CP, where P is the plant response, and C is the controller response. This crossover frequency roughly sets the control bandwidth. For a risetime τ seconds, a good guess for the target bandwidth is 2/τ rad/sec.
To perform PID tuning, the autotuner block measures frequencyresponse information up to a frequency of 10 times the target bandwidth. To ensure that this frequency is less than the Nyquist frequency, the target bandwidth, ω_{c}, must satisfy ω_{c}T_{s} ≤ 0.3, where T_{s} is the controller sample time that you specify with the Controller sample time (sec) parameter. Because of this condition, the fastest rise time you can enforce for tuning is about 6.67T_{s}. If this rise time does not meet your design goals, consider reducing T_{s}.
For best results with closedloop tuning, use a target bandwidth that is within about a factor of 10 of the bandwidth with the initial PID controller. To tune a controller for a larger change in bandwidth, tune incrementally using smaller changes.
To provide the target bandwidth via an input port, select Use external source.
Programmatic Use
Block Parameter:
Bandwidth 
Type: positive scalar 
Default:
1 
Target phase margin (degrees)
— Target minimum phase margin of openloop response
60 (default)  scalar in range 0–90
Specify a target minimum phase margin for the tuned openloop response at the crossover frequency. The target phase margin reflects desired robustness of the tuned system. Typically, choose a value in the range of about 45°–60°. In general, higher phase margin improves overshoot, but can limit response speed. The default value, 60°, tends to balance performance and robustness, yielding about 5–10% overshoot, depending on the characteristics of your plant.
To provide the target phase margin via an input port, select Use external source.
Tunable: Yes
Programmatic Use
Block Parameter: TargetPM 
Type: scalar 
Values: 0–90 
Default: 60 
Experiment Mode
— Sinusoidal perturbation signal type
Superposition
(default)  Sinestream
Specify whether the perturbation at each frequency is applied sequentially (Sinestream) or simultaneously (Superposition).
Sinestream — In this mode, the block applies perturbation at each frequency separately. For more information about sinestream signals for estimation, see Sinestream Input Signals.
Superposition — In this mode, the perturbation signal includes all specified frequencies at once. For frequency response estimation at a vector of frequencies ω = [ω_{1}, … , ω_{N}] at amplitudes A = [A_{1}, … , A_{N}], the perturbation signal is:
$$\Delta u={\displaystyle \sum _{i}{A}_{i}\mathrm{sin}\left({\omega}_{i}t\right)}.$$
Sinestream mode can be more accurate and can also be less intrusive, because the total size of the perturbation is never bigger than the values specified by the Sine Amplitudes parameter. However, due to the sequential nature of the sinestream perturbation, each frequency point you add increases the recommended experiment time (see the start/stop input port for details). Thus, the estimation experiment is typically much faster in Superposition mode with satisfactory results.
Sinestream signals reduce the execution time compared to superposition input signals, but also take longer to estimate the frequency response. Frequency response estimation using sinestream signals is useful when you have limited processing power and you want to reduce the execution time.
Programmatic Use
Block Parameter:
ExperimentMode 
Type: character vector 
Values:
'Superposition' 
'Sinestream' 
Default:
'Superposition' 
Plant Type
— Stability of plant
Stable
(default)  Integrating
Specify whether the plant is stable or integrating. If the plant has one
or more integrators, select Integrating
.
Programmatic Use
Block Parameter:
PlantType 
Type: character vector 
Values:
'Stable' 
'Integrating' 
Default:
'Stable' 
Plant Sign
— Sign of plant
Positive
(default)  Negative
Specify whether the plant is positive or negative. If a positive change in
the plant input at the nominal operating point results in a positive change
in the plant output, specify Positive
. Otherwise,
specify negative. For stable plants, the sign of the plant is the sign of
the plant DC gain.
Programmatic Use
Block Parameter:
PlantSign 
Type: character vector 
Values:
'Positive' 
'Negative' 
Default:
'Positive' 
Sine Amplitudes
— Amplitude of sinusoidal perturbations
1 (default)  scalar  vector of length 5
During the experiment, the block injects a sinusoidal signal into the plant at the frequencies [1/10, 1/3, 1, 3, 10]ω_{c} , where ω_{c} is the target bandwidth for tuning. Use Sine Amplitudes to specify the amplitude of each of these injected signals. Specify a:
Scalar value to inject the same amplitude at each frequency
Vector of length 5 to specify a different amplitude at each of [1/10, 1/3, 1, 3, 10]ω_{c}
In a typical plant with typical target bandwidth, the magnitudes of the plant responses at the experiment frequencies do not vary widely. In such cases, you can use a scalar value to apply the same magnitude perturbation at all frequencies. However, if you know that the response decays sharply over the frequency range, consider decreasing the amplitude of the lowerfrequency inputs and increasing the amplitude of the higherfrequency inputs. It is numerically better for the estimation experiment when all the plant responses have comparable magnitudes.
The perturbation amplitudes must be:
Large enough that the perturbation overcomes any deadband in the plant actuator and generates a response above the noise level
Small enough to keep the plant running within the approximately linear region near the nominal operating point, and to avoid saturating the plant input or output
When Experiment mode is Superposition, the sinusoidal signals are superimposed. Thus, the perturbation can be at least as large as the sum of all amplitudes. Make sure that the largest possible perturbation is within the range of your plant actuator. Saturating the actuator can introduce errors into the estimated frequency response.
To provide the sine amplitudes via an input port, select Use external source.
Tunable: Yes
Programmatic Use
Block Parameter:
AmpSine 
Type: scalar, vector of length 5 
Default: 1 
Reduce memory and avoid task overrun (external mode only)
— Deploy tuning algorithm only
off
(default)  on
The block contains two modules, one that performs the realtime frequencyresponse estimation, and one that uses the resulting estimated response to tune the PID gains. When you run a Simulink model containing the block in the external simulation mode, by default both modules are deployed. You can save memory on the target hardware by deploying the estimation module only (see Control RealTime PID Autotuning in Simulink). In this case, the tuning algorithm runs on the Simulink host computer instead of the target hardware. When this option is selected, the deployed algorithm uses about a third as much memory as when the option is cleared.
The PID gain calculation demands more computational load than the frequencyresponse estimation. For fast controller sample times, some hardware might not finish the gain calculation within one execution cycle. Therefore, when using hardware with limited computing power, selecting this option lets you tune a PID controller with a fast sample time.
Additionally, when you enable this option, there can be a delay of several sampling periods between when the tuning experiment ends and when the new PID gains arrive at the pid gains output port. Before pushing gains to the controller, first confirm the change at the pid gains output port instead of using start/stop signal as the trigger for the update.
If you intend to deploy the block and perform PID tuning without using external simulation mode, do not select this option.
Caution
When you use this option, the model must be configured such that numeric block parameters are tunable in generated code, not inlined. To specify tunable parameters:
In the model editor: In Configuration Parameters, in Code Generation > Optimization, set Default parameter behavior to
Tunable
.At the command line: Use
set_param(mdl,'DefaultParameterBehavior','Tunable')
.
Programmatic Use
Block Parameter:
DeployTuningModule 
Type: character vector 
Values:
'off'  'on'

Default:
'off' 
Configure block for PLC Coder
— Configure block for code generation with Simulink PLC Coder
off (default)  on
Select this parameter if you are using Simulink PLC Coder to generate code for the autotuner block. Clear the parameter for code generation with any other MathWorks^{®} codegeneration product.
Selecting this parameter affects internal block configuration only, for compatibility with Simulink PLC Coder. The parameter has no operative effect on generated code.
Output Signal Configuration
— Provide control signal plus perturbation or perturbation only
control action + perturbation (default)  perturbation only
By default, the block takes a control signal as input and provides the control signal plus the experiment perturbation at the port u+Δu. You then feed this signal into the plant input, as shown in the following diagram.
This default configuration requires inserting the block between the controller and the plant. If you want to add the perturbation signal to the control signal yourself, select perturbation only. In this configuration, the block output contains the perturbation signal only, at the port Δu. You inject this perturbation signal into the plant using, for example, a sum block, as in the following diagram.
In this configuration, you can optionally comment out the ClosedLoop PID Autotuner block without disrupting the model.
Data Type
— Floating point precision
double
(default)  single
Specify the floatingpoint precision based on simulation environment or hardware requirements.
Programmatic Use
Block Parameter:
BlockDataType 
Type: character vector 
Values:
'double'  'single'

Default:
'double' 
Clicking "Update PID Block" writes tuned gains to the PID block connected to "u" port
— Automatically detect target for writing tuned PID coefficients
on
(default)  off
Under some conditions, the autotuner block can write tuned gains to a standard or custom PID controller block. To indicate that the target PID controller is the block connected to the u port of the autotuner block, select this option. To specify a PID controller that is not connected to u, clear this option.
To write tuned gains from the autotuner block to a PID controller anywhere in the model, the target block must be either:
A PID Controller or Discrete PID Controller block.
A masked subsystem in which the PID coefficients are mask parameters named
P
,I
,D
, andN
, or whatever subset of these parameters exist in the your controller. For example, if you use a custom PI controller, then you only need mask parametersP
andI
.
Specify PID block path
— Target PID controller block for writing tuned coefficients
[]
(default)  block path
Under some conditions, the autotuner block can write tuned gains to a standard or custom PID controller block. Use this parameter to specify the path of the target PID controller.
To write tuned gains from the autotuner block to a PID controller anywhere in the model, the target block must be either:
A PID Controller or Discrete PID Controller block.
A masked subsystem in which the PID coefficients are mask parameters named
P
,I
,D
, andN
, or whatever subset of these parameters exist in your controller
Dependencies
This parameter is enabled when Clicking "Update PID Block" writes tuned gains to the PID block connected to "u" port is selected.
Update PID Block
— Write tuned PID gains to target controller block
button
The block does not automatically push the tuned gains to the target PID block. If your PID controller block meets the criteria described in the Specify PID block path
parameter description, after tuning, click this button to transfer the tuned gains to the block.
You can update the PID block while the simulation is running, including when running in external mode. Doing so is useful for immediately validating tuned PID gains. At any time during simulation, you can change parameters, start the experiment again, and push the new tuned gains to the PID block. You can then continue to run the model and observe the behavior of your plant.
Export to MATLAB
— Send experiment and tuning results to MATLAB workspace
button
When you click this button, the block creates a structure in the
MATLAB^{®} workspace containing the experiment and tuning results. This
structure, OnlinePIDTuningResult
, contains the following
fields:
P
,I
,D
,N
— Tuned PID gains. The structure contains whichever of these fields are necessary for the controller type you are tuning. For instance, if you are tuning a PI controller, the structure containsP
andI
, but notD
andN
.TargetBandwidth
— The value you specified in the Target bandwidth (rad/sec) parameter of the block.TargetPhaseMargin
— The value you specified in the Target phase margin (degrees) parameter of the block.EstimatedPhaseMargin
— Estimated phase margin achieved by the tuned system.Controller
— The tuned PID controller, returned as apid
(for parallel form) orpidstd
(for ideal form) model object.Plant
— The estimated plant, returned as anfrd
model object. Thisfrd
contains the response data obtained at the experiment frequencies [1/10, 1/3, 1, 3, 10]ω_{c}.PlantNominal
— The plant input and output at the nominal operating point when the experiment begins, specified as a structure having fieldsu
(input) andy
(output).
You can export to the MATLAB workspace while the simulation is running, including when running in external mode.
Model Examples
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.
PLC Code Generation
Generate Structured Text code using Simulink® PLC Coder™.
Version History
Introduced in R2018a
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