# tf

Transfer function model

## Description

Use tf to create real-valued or complex-valued transfer function models, or to convert dynamic system models to transfer function form.

Transfer functions are a frequency-domain representation of linear time-invariant systems. For instance, consider a continuous-time SISO dynamic system represented by the transfer function sys(s) = N(s)/D(s), where s = jw and N(s) and D(s) are called the numerator and denominator polynomials, respectively. The tf model object can represent SISO or MIMO transfer functions in continuous time or discrete time.

You can create a transfer function model object either by specifying its coefficients directly, or by converting a model of another type (such as a state-space model ss) to transfer-function form. For more information, see Transfer Functions.

You can also use tf to create generalized state-space (genss) models or uncertain state-space (uss (Robust Control Toolbox)) models.

## Creation

### Description

example

sys = tf(numerator,denominator) creates a continuous-time transfer function model, setting the Numerator and Denominator properties. For instance, consider a continuous-time SISO dynamic system represented by the transfer function sys(s) = N(s)/D(s), the input arguments numerator and denominator are the coefficients of N(s) and D(s), respectively.

example

sys = tf(numerator,denominator,ts) creates a discrete-time transfer function model, setting the Numerator, Denominator, and Ts properties. For instance, consider a discrete-time SISO dynamic system represented by the transfer function sys(z) = N(z)/D(z), the input arguments numerator and denominator are the coefficients of N(z) and D(z), respectively. To leave the sample time unspecified, set ts input argument to -1.

example

sys = tf(numerator,denominator,ltiSys) creates a transfer function model with properties inherited from the dynamic system model ltiSys, including the sample time.

example

sys = tf(m) creates a transfer function model that represents the static gain, m.

example

sys = tf(___,Name,Value) sets properties of the transfer function model using one or more Name,Value pair arguments for any of the previous input-argument combinations.

example

sys = tf(ltiSys) converts the dynamic system model ltiSys to a transfer function model.

example

sys = tf(ltiSys,component) converts the specified component of ltiSys to transfer function form. Use this syntax only when ltiSys is an identified linear time-invariant (LTI) model.

example

s = tf('s') creates special variable s that you can use in a rational expression to create a continuous-time transfer function model. Using a rational expression can sometimes be easier and more intuitive than specifying polynomial coefficients.

example

z = tf('z',ts) creates special variable z that you can use in a rational expression to create a discrete-time transfer function model. To leave the sample time unspecified, set ts input argument to -1.

### Input Arguments

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Numerator coefficients of the transfer function, specified as:

• A row vector of polynomial coefficients.

• An Ny-by-Nu cell array of row vectors to specify a MIMO transfer function, where Ny is the number of outputs, and Nu is the number of inputs.

When you create the transfer function, specify the numerator coefficients in order of descending power. For instance, if the transfer function numerator is 3s^2-4s+5, then specify numerator as [3 -4 5]. For a discrete-time transfer function with numerator 2z-1, set numerator to [2 -1].

Also a property of the tf object. For more information, see Numerator.

Denominator coefficients, specified as:

• A row vector of polynomial coefficients.

• An Ny-by-Nu cell array of row vectors to specify a MIMO transfer function, where Ny is the number of outputs and Nu is the number of inputs.

When you create the transfer function, specify the denominator coefficients in order of descending power. For instance, if the transfer function denominator is 7s^2+8s-9, then specify denominator as [7 8 -9]. For a discrete-time transfer function with denominator 2z^2+1, set denominator to [2 0 1].

Also a property of the tf object. For more information, see Denominator.

Sample time, specified as a scalar. Also a property of the tf object. For more information, see Ts.

Dynamic system, specified as a SISO or MIMO dynamic system model or array of dynamic system models. Dynamic systems that you can use include:

• Continuous-time or discrete-time numeric LTI models, such as tf, zpk, ss, or pid models.

• Generalized or uncertain LTI models such as genss or uss (Robust Control Toolbox) models. (Using uncertain models requires Robust Control Toolbox™ software.)

The resulting transfer function assumes

• current values of the tunable components for tunable control design blocks.

• nominal model values for uncertain control design blocks.

• Identified LTI models, such as idtf (System Identification Toolbox), idss (System Identification Toolbox), idproc (System Identification Toolbox), idpoly (System Identification Toolbox), and idgrey (System Identification Toolbox) models. To select the component of the identified model to convert, specify component. If you do not specify component, tf converts the measured component of the identified model by default. (Using identified models requires System Identification Toolbox™ software.)

Static gain, specified as a scalar or matrix. Static gain or steady state gain of a system represents the ratio of the output to the input under steady state condition.

Component of identified model to convert, specified as one of the following:

• 'measured' — Convert the measured component of sys.

• 'noise' — Convert the noise component of sys

• 'augmented' — Convert both the measured and noise components of sys.

component only applies when sys is an identified LTI model.

For more information on identified LTI models and their measured and noise components, see Identified LTI Models.

### Output Arguments

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Output system model, returned as:

• A transfer function (tf) model object, when numerator and denominator input arguments are numeric arrays.

• A generalized state-space model (genss) object, when the numerator or denominator input arguments includes tunable parameters, such as realp parameters or generalized matrices (genmat). For an example, see Tunable Low-Pass Filter.

• An uncertain state-space model (uss) object, when the numerator or denominator input arguments includes uncertain parameters. Using uncertain models requires Robust Control Toolbox software. For an example, see Transfer Function with Uncertain Coefficients (Robust Control Toolbox).

## Properties

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Numerator coefficients, specified as:

• A row vector of polynomial coefficients in order of descending power (for Variable values 's', 'z', 'p', or 'q') or in order of ascending power (for Variable values 'z^-1' or 'q^-1').

• An Ny-by-Nu cell array of row vectors to specify a MIMO transfer function, where Ny is the number of outputs and Nu is the number of inputs. Each element of the cell array specifies the numerator coefficients for a given input/output pair. If you specify both Numerator and Denominator as cell arrays, they must have the same dimensions.

The coefficients of Numerator can be either real-valued or complex-valued.

Denominator coefficients, specified as:

• A row vector of polynomial coefficients in order of descending power (for values Variable values 's', 'z', 'p', or 'q') or in order of ascending power (for Variable values 'z^-1' or 'q^-1').

• An Ny-by-Nu cell array of row vectors to specify a MIMO transfer function, where Ny is the number of outputs and Nu is the number of inputs. Each element of the cell array specifies the numerator coefficients for a given input/ output pair. If you specify both Numerator and Denominator as cell arrays, they must have the same dimensions.

If all SISO entries of a MIMO transfer function have the same denominator, you can specify Denominator as the row vector while specifying Numerator as a cell array.

The coefficients of Denominator can be either real-valued or complex-valued.

Transfer function display variable, specified as one of the following:

• 's' — Default for continuous-time models

• 'z' — Default for discrete-time models

• 'p' — Equivalent to 's'

• 'q' — Equivalent to 'z'

• 'z^-1' — Inverse of 'z'

• 'q^-1' — Equivalent to 'z^-1'

The value of Variable is reflected in the display, and also affects the interpretation of the Numerator and Denominator coefficient vectors for discrete-time models.

• For Variable values 's', 'z', 'p', or 'q', the coefficients are ordered in descending powers of the variable. For example, consider the row vector [ak ... a1 a0]. The polynomial order is specified as ${a}_{k}{z}^{k}+...+{a}_{1}z+{a}_{0}$.

• For Variable values 'z^-1' or 'q^-1', the coefficients are ordered in ascending powers of the variable. For example, consider the row vector [b0 b1 ... bk]. The polynomial order is specified as ${b}_{0}+{b}_{1}{z}^{-1}+...+{b}_{k}{z}^{-k}$.

Transport delay, specified as one of the following:

• Scalar — Specify the transport delay for a SISO system or the same transport delay for all input/output pairs of a MIMO system.

• Ny-by-Nu array — Specify separate transport delays for each input/output pair of a MIMO system. Here, Ny is the number of outputs and Nu is the number of inputs.

For continuous-time systems, specify transport delays in the time unit specified by the TimeUnit property. For discrete-time systems, specify transport delays in integer multiples of the sample time, Ts.

Input delay for each input channel, specified as one of the following:

• Scalar — Specify the input delay for a SISO system or the same delay for all inputs of a multi-input system.

• Nu-by-1 vector — Specify separate input delays for input of a multi-input system, where Nu is the number of inputs.

For continuous-time systems, specify input delays in the time unit specified by the TimeUnit property. For discrete-time systems, specify input delays in integer multiples of the sample time, Ts.

Output delay for each output channel, specified as one of the following:

• Scalar — Specify the output delay for a SISO system or the same delay for all outputs of a multi-output system.

• Ny-by-1 vector — Specify separate output delays for output of a multi-output system, where Ny is the number of outputs.

For continuous-time systems, specify output delays in the time unit specified by the TimeUnit property. For discrete-time systems, specify output delays in integer multiples of the sample time, Ts.

Sample time, specified as:

• 0 for continuous-time systems.

• A positive scalar representing the sampling period of a discrete-time system. Specify Ts in the time unit specified by the TimeUnit property.

• -1 for a discrete-time system with an unspecified sample time.

Note

Changing Ts does not discretize or resample the model. To convert between continuous-time and discrete-time representations, use c2d and d2c. To change the sample time of a discrete-time system, use d2d.

Time variable units, specified as one of the following:

• 'nanoseconds'

• 'microseconds'

• 'milliseconds'

• 'seconds'

• 'minutes'

• 'hours'

• 'days'

• 'weeks'

• 'months'

• 'years'

Changing TimeUnit has no effect on other properties, but changes the overall system behavior. Use chgTimeUnit to convert between time units without modifying system behavior.

Input channel names, specified as one of the following:

• A character vector, for single-input models.

• A cell array of character vectors, for multi-input models.

• '', no names specified for any input channels.

Alternatively, you can assign input names for multi-input models using automatic vector expansion. For example, if sys is a two-input model, enter:

sys.InputName = 'controls';

The input names automatically expand to {'controls(1)';'controls(2)'}.

You can use the shorthand notation u to refer to the InputName property. For example, sys.u is equivalent to sys.InputName.

Use InputName to:

• Identify channels on model display and plots.

• Extract subsystems of MIMO systems.

• Specify connection points when interconnecting models.

Input channel units, specified as one of the following:

• A character vector, for single-input models.

• A cell array of character vectors, for multi-input models.

• '', no units specified for any input channels.

Use InputUnit to specify input signal units. InputUnit has no effect on system behavior.

Input channel groups, specified as a structure. Use InputGroup to assign the input channels of MIMO systems into groups and refer to each group by name. The field names of InputGroup are the group names and the field values are the input channels of each group. For example:

sys.InputGroup.controls = [1 2];
sys.InputGroup.noise = [3 5];

creates input groups named controls and noise that include input channels 1 and 2, and 3 and 5, respectively. You can then extract the subsystem from the controls inputs to all outputs using:

sys(:,'controls')

By default, InputGroup is a structure with no fields.

Output channel names, specified as one of the following:

• A character vector, for single-output models.

• A cell array of character vectors, for multi-output models.

• '', no names specified for any output channels.

Alternatively, you can assign output names for multi-output models using automatic vector expansion. For example, if sys is a two-output model, enter:

sys.OutputName = 'measurements';

The output names automatically expand to {'measurements(1)';'measurements(2)'}.

You can also use the shorthand notation y to refer to the OutputName property. For example, sys.y is equivalent to sys.OutputName.

Use OutputName to:

• Identify channels on model display and plots.

• Extract subsystems of MIMO systems.

• Specify connection points when interconnecting models.

Output channel units, specified as one of the following:

• A character vector, for single-output models.

• A cell array of character vectors, for multi-output models.

• '', no units specified for any output channels.

Use OutputUnit to specify output signal units. OutputUnit has no effect on system behavior.

Output channel groups, specified as a structure. Use OutputGroupto assign the output channels of MIMO systems into groups and refer to each group by name. The field names of OutputGroup are the group names and the field values are the output channels of each group. For example:

sys.OutputGroup.temperature = [1];
sys.InputGroup.measurement = [3 5];

creates output groups named temperature and measurement that include output channels 1, and 3 and 5, respectively. You can then extract the subsystem from all inputs to the measurement outputs using:

sys('measurement',:)

By default, OutputGroup is a structure with no fields.

System name, specified as a character vector. For example, 'system_1'.

User-specified text that you want to associate with the system, specified as a character vector or cell array of character vectors. For example, 'System is MIMO'.

User-specified data that you want to associate with the system, specified as any MATLAB data type.

Sampling grid for model arrays, specified as a structure array.

Use SamplingGrid to track the variable values associated with each model in a model array, including identified linear time-invariant (IDLTI) model arrays.

Set the field names of the structure to the names of the sampling variables. Set the field values to the sampled variable values associated with each model in the array. All sampling variables must be numeric scalars, and all arrays of sampled values must match the dimensions of the model array.

For example, you can create an 11-by-1 array of linear models, sysarr, by taking snapshots of a linear time-varying system at times t = 0:10. The following code stores the time samples with the linear models.

sysarr.SamplingGrid = struct('time',0:10)

Similarly, you can create a 6-by-9 model array, M, by independently sampling two variables, zeta and w. The following code maps the (zeta,w) values to M.

[zeta,w] = ndgrid(<6 values of zeta>,<9 values of w>)
M.SamplingGrid = struct('zeta',zeta,'w',w)

When you display M, each entry in the array includes the corresponding zeta and w values.

M
M(:,:,1,1) [zeta=0.3, w=5] =

25
--------------
s^2 + 3 s + 25

M(:,:,2,1) [zeta=0.35, w=5] =

25
----------------
s^2 + 3.5 s + 25

...

For model arrays generated by linearizing a Simulink® model at multiple parameter values or operating points, the software populates SamplingGrid automatically with the variable values that correspond to each entry in the array. For instance, the Simulink Control Design™ commands linearize (Simulink Control Design) and slLinearizer (Simulink Control Design) populate SamplingGrid automatically.

By default, SamplingGrid is a structure with no fields.

## Object Functions

The following lists contain a representative subset of the functions you can use with tf models. In general, any function applicable to Dynamic System Models is applicable to a tf object.

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 step Step response plot of dynamic system; step response data impulse Impulse response plot of dynamic system; impulse response data lsim Plot simulated time response of dynamic system to arbitrary inputs; simulated response data bode Bode plot of frequency response, or magnitude and phase data nyquist Nyquist plot of frequency response nichols Nichols chart of frequency response bandwidth Frequency response bandwidth
 pole Poles of dynamic system zero Zeros and gain of SISO dynamic system pzplot Pole-zero plot of dynamic system model with plot customization options margin Gain margin, phase margin, and crossover frequencies
 zpk Zero-pole-gain model ss State-space model c2d Convert model from continuous to discrete time d2c Convert model from discrete to continuous time d2d Resample discrete-time model
 feedback Feedback connection of multiple models connect Block diagram interconnections of dynamic systems series Series connection of two models parallel Parallel connection of two models
 pidtune PID tuning algorithm for linear plant model rlocus Root locus plot of dynamic system lqr Linear-Quadratic Regulator (LQR) design lqg Linear-Quadratic-Gaussian (LQG) design lqi Linear-Quadratic-Integral control kalman Design Kalman filter for state estimation

## Examples

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For this example, consider the following SISO transfer function model:

$sys\left(s\right)=\frac{1}{2{s}^{2}+3s+4}.$

Specify the numerator and denominator coefficients ordered in descending powers of s, and create the transfer function model.

numerator = 1;
denominator = [2,3,4];
sys = tf(numerator,denominator)
sys =

1
---------------
2 s^2 + 3 s + 4

Continuous-time transfer function.

For this example, consider the following discrete-time SISO transfer function model:

$sys\left(z\right)=\frac{2z}{4{z}^{3}+3z-1}.$

Specify the numerator and denominator coefficients ordered in descending powers of z and the sample time of 0.1 seconds. Create the discrete-time transfer function model.

numerator = [2,0];
denominator = [4,0,3,-1];
ts = 0.1;
sys = tf(numerator,denominator,ts)
sys =

2 z
---------------
4 z^3 + 3 z - 1

Sample time: 0.1 seconds
Discrete-time transfer function.

For this example, consider a transfer function model that represents a second-order system with known natural frequency and damping ratio.

The transfer function of a second-order system, expressed in terms of its damping ratio $\zeta$ and natural frequency ${\omega }_{0}$, is:

$sys\left(s\right)=\frac{{\omega }_{0}^{2}}{{s}^{2}+2\zeta {\omega }_{0}s+{\omega }_{0}^{2}}.$

Assuming a damping ratio, $\zeta$ = 0.25 and natural frequency, ${\omega }_{0}$ = 3 rad/s, create the second order transfer function.

zeta = 0.25;
w0 = 3;
numerator = w0^2;
denominator = [1,2*zeta*w0,w0^2];
sys = tf(numerator,denominator)
sys =

9
---------------
s^2 + 1.5 s + 9

Continuous-time transfer function.

Examine the response of this transfer function to a step input.

stepplot(sys)

The plot shows the ringdown expected of a second-order system with a low damping ratio.

Create a transfer function for the discrete-time, multi-input, multi-output model:

$sys\left(z\right)=\left[\begin{array}{cc}\frac{1}{z+0.3}& \frac{z}{z+0.3}\\ \frac{-z+2}{z+0.3}& \frac{3}{z+0.3}\end{array}\right]$

with sample time ts = 0.2 seconds.

Specify the numerator coefficients as a 2-by-2 matrix.

numerators = {1 [1 0];[-1 2] 3};

Specify the coefficients of the common denominator as a row vector.

denominator = [1 0.3];

Create the discrete-time MIMO transfer function model.

ts = 0.2;
sys = tf(numerators,denominator,ts)
sys =

From input 1 to output...
1
1:  -------
z + 0.3

-z + 2
2:  -------
z + 0.3

From input 2 to output...
z
1:  -------
z + 0.3

3
2:  -------
z + 0.3

Sample time: 0.2 seconds
Discrete-time transfer function.

For more information on creating MIMO transfer functions, see MIMO Transfer Functions.

In this example, you create a MIMO transfer function model by concatenating SISO transfer function models. Consider the following single-input, two-output transfer function:

$sys\left(s\right)=\left[\begin{array}{c}\frac{s-1}{s+1}\\ \frac{s+2}{{s}^{2}+4s+5}\end{array}\right].$

Specify the MIMO transfer function model by concatenating the SISO entries.

sys1 = tf([1 -1],[1 1]);
sys2 = tf([1 2],[1 4 5]);
sys = [sys1;sys2]
sys =

From input to output...
s - 1
1:  -----
s + 1

s + 2
2:  -------------
s^2 + 4 s + 5

Continuous-time transfer function.

For more information on creating MIMO transfer functions, see MIMO Transfer Functions.

For this example, create a continuous-time transfer function model using rational expressions. Using a rational expression can sometimes be easier and more intuitive than specifying polynomial coefficients of the numerator and denominator.

Consider the following system:

$sys\left(s\right)=\frac{s}{{s}^{2}+2s+10}.$

To create the transfer function model, first specify s as a tf object.

s = tf('s')
s =

s

Continuous-time transfer function.

Create the transfer function model using s in the rational expression.

sys = s/(s^2 + 2*s + 10)
sys =

s
--------------
s^2 + 2 s + 10

Continuous-time transfer function.

For this example, create a discrete-time transfer function model using a rational expression. Using a rational expression can sometimes be easier and more intuitive than specifying polynomial coefficients.

Consider the following system:

$sys\left(z\right)=\frac{z-1}{{z}^{2}-1.85z+0.9}.$

To create the transfer function model, first specify z as a tf object and the sample time Ts.

ts = 0.1;
z = tf('z',ts)
z =

z

Sample time: 0.1 seconds
Discrete-time transfer function.

Create the transfer function model using z in the rational expression.

sys = (z - 1) / (z^2 - 1.85*z + 0.9)
sys =

z - 1
------------------
z^2 - 1.85 z + 0.9

Sample time: 0.1 seconds
Discrete-time transfer function.

For this example, create a transfer function model with properties inherited from another transfer function model. Consider the following two transfer functions:

$sys1\left(s\right)=\frac{2s}{{s}^{2}+8s}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}and\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}sys2\left(s\right)=\frac{s-1}{7{s}^{4}+2{s}^{3}+9}.$

For this example, create sys1 with the TimeUnit and InputDelay property set to 'minutes'.

numerator1 = [2,0];
denominator1 = [1,8,0];
sys1 = tf(numerator1,denominator1,'TimeUnit','minutes','InputUnit','minutes')
sys1 =

2 s
---------
s^2 + 8 s

Continuous-time transfer function.
propValues1 = [sys1.TimeUnit,sys1.InputUnit]
propValues1 = 1x2 cell
{'minutes'}    {'minutes'}

Create the second transfer function model with properties inherited from sys1.

numerator2 = [1,-1];
denominator2 = [7,2,0,0,9];
sys2 = tf(numerator2,denominator2,sys1)
sys2 =

s - 1
-----------------
7 s^4 + 2 s^3 + 9

Continuous-time transfer function.
propValues2 = [sys2.TimeUnit,sys2.InputUnit]
propValues2 = 1x2 cell
{'minutes'}    {'minutes'}

Observe that the transfer function model sys2 has that same properties as sys1.

You can use a for loop to specify an array of transfer function models.

First, pre-allocate the transfer function array with zeros.

sys = tf(zeros(1,1,3));

The first two indices represent the number of outputs and inputs for the models, while the third index is the number of models in the array.

Create the transfer function model array using a rational expression in the for loop.

s = tf('s');
for k = 1:3
sys(:,:,k) = k/(s^2+s+k);
end
sys
sys(:,:,1,1) =

1
-----------
s^2 + s + 1

sys(:,:,2,1) =

2
-----------
s^2 + s + 2

sys(:,:,3,1) =

3
-----------
s^2 + s + 3

3x1 array of continuous-time transfer functions.

For this example, compute the transfer function of the following state-space model:

$A=\left[\begin{array}{cc}-2& -1\\ 1& -2\end{array}\right],\phantom{\rule{1em}{0ex}}B=\left[\begin{array}{cc}1& 1\\ 2& -1\end{array}\right],\phantom{\rule{1em}{0ex}}C=\left[\begin{array}{cc}1& 0\end{array}\right],\phantom{\rule{1em}{0ex}}D=\left[\phantom{\rule{0.1em}{0ex}}\begin{array}{cc}0& 1\end{array}\right].$

Create the state-space model using the state-space matrices.

A = [-2 -1;1 -2];
B = [1 1;2 -1];
C = [1 0];
D = [0 1];
ltiSys = ss(A,B,C,D);

Convert the state-space model ltiSys to a transfer function.

sys = tf(ltiSys)
sys =

From input 1 to output:
s + 6.28e-16
-------------
s^2 + 4 s + 5

From input 2 to output:
s^2 + 5 s + 8
-------------
s^2 + 4 s + 5

Continuous-time transfer function.

For this example, extract the measured and noise components of an identified polynomial model into two separate transfer functions.

Load the Box-Jenkins polynomial model ltiSys in identifiedModel.mat.

ltiSys is an identified discrete-time model of the form: $y\left(t\right)=\frac{B}{F}u\left(t\right)+\frac{C}{D}e\left(t\right)$, where $\frac{B}{F}$ represents the measured component and $\frac{C}{D}$ the noise component.

Extract the measured and noise components as transfer functions.

sysMeas = tf(ltiSys,'measured')
sysMeas =

From input "u1" to output "y1":
-0.1426 z^-1 + 0.1958 z^-2
z^(-2) * ----------------------------
1 - 1.575 z^-1 + 0.6115 z^-2

Sample time: 0.04 seconds
Discrete-time transfer function.
sysNoise = tf(ltiSys,'noise')
sysNoise =

From input "v@y1" to output "y1":
0.04556 + 0.03301 z^-1
----------------------------------------
1 - 1.026 z^-1 + 0.26 z^-2 - 0.1949 z^-3

Input groups:
Name     Channels
Noise       1

Sample time: 0.04 seconds
Discrete-time transfer function.

The measured component can serve as a plant model, while the noise component can be used as a disturbance model for control system design.

Transfer function model objects include model data that helps you keep track of what the model represents. For instance, you can assign names to the inputs and outputs of your model.

Consider the following continuous-time MIMO transfer function model:

$sys\left(s\right)=\left[\begin{array}{c}\frac{s+1}{{s}^{2}+2s+2}\\ \frac{1}{s}\end{array}\right]$

The model has one input $-$ Current, and two outputs $-$ Torque and Angular velocity.

First, specify the numerator and denominator coefficients of the model.

numerators = {[1 1] ; 1};
denominators = {[1 2 2] ; [1 0]};

Create the transfer function model, specifying the input name and output names.

sys = tf(numerators,denominators,'InputName','Current',...
'OutputName',{'Torque' 'Angular Velocity'})
sys =

From input "Current" to output...
s + 1
Torque:  -------------
s^2 + 2 s + 2

1
Angular Velocity:  -
s

Continuous-time transfer function.

For this example, specify polynomial ordering in discrete-time transfer function models using the 'Variable' property.

Consider the following discrete-time transfer functions with sample time 0.1 seconds:

$sys1\left(z\right)=\frac{{z}^{2}}{{z}^{2}+2z+3}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}sys2\left({z}^{-1}\right)=\frac{1}{1+2{z}^{-1}+3{z}^{-2}}.$

Create the first discrete-time transfer function by specifying the z coefficients.

numerator = [1,0,0];
denominator = [1,2,3];
ts = 0.1;
sys1 = tf(numerator,denominator,ts)
sys1 =

z^2
-------------
z^2 + 2 z + 3

Sample time: 0.1 seconds
Discrete-time transfer function.

The coefficients of sys1 are ordered in descending powers of z.

tf switches convention based on the value of the 'Variable' property. Since sys2 is the inverse transfer function model of sys1, specify 'Variable' as 'z^-1' and use the same numerator and denominator coefficients.

sys2 = tf(numerator,denominator,ts,'Variable','z^-1')
sys2 =

1
-------------------
1 + 2 z^-1 + 3 z^-2

Sample time: 0.1 seconds
Discrete-time transfer function.

The coefficients of sys2 are now ordered in ascending powers of z^-1.

Based on different conventions, you can specify polynomial ordering in transfer function models using the 'Variable' property.

In this example, you will create a low-pass filter with one tunable parameter a:

$F=\frac{a}{s+a}$

Since the numerator and denominator coefficients of a tunableTF block are independent, you cannot use tunableTF to represent F. Instead, construct F using the tunable real parameter object realp.

Create a real tunable parameter with an initial value of 10.

a = realp('a',10)
a =
Name: 'a'
Value: 10
Minimum: -Inf
Maximum: Inf
Free: 1

Real scalar parameter.

Use tf to create the tunable low-pass filter F.

numerator = a;
denominator = [1,a];
F = tf(numerator,denominator)
F =

Generalized continuous-time state-space model with 1 outputs, 1 inputs, 1 states, and the following blocks:
a: Scalar parameter, 2 occurrences.

Type "ss(F)" to see the current value, "get(F)" to see all properties, and "F.Blocks" to interact with the blocks.

F is a genss object which has the tunable parameter a in its Blocks property. You can connect F with other tunable or numeric models to create more complex control system models. For an example, see Control System with Tunable Components.

In this example, you will create a static gain MIMO transfer function model.

Consider the following two-input, two-output static gain matrix m:

$m=\left[\begin{array}{cc}2& 4\\ 3& 5\end{array}\right]$

Specify the gain matrix and create the static gain transfer function model.

m = [2,4;...
3,5];
sys1 = tf(m)
sys1 =

From input 1 to output...
1:  2

2:  3

From input 2 to output...
1:  4

2:  5

Static gain.

You can use static gain transfer function model sys1 obtained above to cascade it with another transfer function model.

For this example, create another two-input, two-output discrete transfer function model and use the series function to connect the two models.

numerators = {1,[1,0];[-1,2],3};
denominator = [1,0.3];
ts = 0.2;
sys2 = tf(numerators,denominator,ts)
sys2 =

From input 1 to output...
1
1:  -------
z + 0.3

-z + 2
2:  -------
z + 0.3

From input 2 to output...
z
1:  -------
z + 0.3

3
2:  -------
z + 0.3

Sample time: 0.2 seconds
Discrete-time transfer function.
sys = series(sys1,sys2)
sys =

From input 1 to output...
3 z^2 + 2.9 z + 0.6
1:  -------------------
z^2 + 0.6 z + 0.09

-2 z^2 + 12.4 z + 3.9
2:  ---------------------
z^2 + 0.6 z + 0.09

From input 2 to output...
5 z^2 + 5.5 z + 1.2
1:  -------------------
z^2 + 0.6 z + 0.09

-4 z^2 + 21.8 z + 6.9
2:  ---------------------
z^2 + 0.6 z + 0.09

Sample time: 0.2 seconds
Discrete-time transfer function.

## Limitations

• Transfer function models are ill-suited for numerical computations. Once created, convert them to state-space form before combining them with other models or performing model transformations. You can then convert the resulting models back to transfer function form for inspection purposes

• An identified nonlinear model cannot be directly converted into a transfer function model using tf. To obtain a transfer function model:

1. Convert the nonlinear identified model to an identified LTI model using linapp (System Identification Toolbox), idnlarx/linearize (System Identification Toolbox), or idnlhw/linearize (System Identification Toolbox).

2. Then, convert the resulting model to a transfer function model using tf.