# ss

State-space model

## Description

Use `ss`

to create real-valued or complex-valued state-space
models, or to convert dynamic system models to
state-space model form.

A state-space model is a mathematical representation of a physical system as a set of
input, output, and state variables related by first-order differential equations. The state
variables define the values of the output variables. The `ss`

model object
can represent SISO or MIMO state-space models in continuous time or discrete time.

In continuous-time, a state-space model is of the following form:

$$\begin{array}{l}\dot{x}=Ax+Bu\\ y=Cx+Du\end{array}$$

Here, `x`

, `u`

and `y`

represent the states, inputs and outputs respectively, while `A`

,
`B`

, `C`

and `D`

are the state-space
matrices. The `ss`

object represents a state-space model in MATLAB^{®} storing `A`

, `B`

, `C`

and
`D`

along with other information such as sample time, I/O names, delays,
and offsets.

You can create a state-space model object by either specifying the state, input and output
matrices directly, or by converting a model of another type (such as a transfer function model
`tf`

) to state-space form. For more information, see State-Space Models. You
can use an `ss`

model object to:

Perform linear analysis

Represent a linear time-invariant (LTI) model to perform control design

Combine with other LTI models to represent a more complex system

## Creation

### Syntax

### Description

creates a continuous-time state-space model object of the following form:`sys`

= ss(`A`

,`B`

,`C`

,`D`

)

$$\begin{array}{l}\dot{x}=Ax+Bu\\ y=Cx+Du\end{array}$$

For instance, consider a plant with `Nx`

states,
`Ny`

outputs, and `Nu`

inputs. The state-space
matrices are:

`A`

is an`Nx`

-by-`Nx`

real- or complex-valued matrix.`B`

is an`Nx`

-by-`Nu`

real- or complex-valued matrix.`C`

is an`Ny`

-by-`Nx`

real- or complex-valued matrix.`D`

is an`Ny`

-by-`Nu`

real- or complex-valued matrix.

converts to `sys`

= ss(`ltiSys`

,`component`

)`ss`

object form the measured component, the noise
component or both of specified `component`

of an identified linear
time-invariant (LTI) model `ltiSys`

. Use this syntax only when
`ltiSys`

is an identified (LTI) model such as an `idtf`

(System Identification Toolbox), `idss`

(System Identification Toolbox), `idproc`

(System Identification Toolbox), `idpoly`

(System Identification Toolbox) or `idgrey`

(System Identification Toolbox) object.

returns the minimal state-space realization with no uncontrollable or unobservable
states. This realization is equivalent to `sys`

= ss(`ssSys`

,'minimal')`minreal(ss(sys))`

where
matrix `A`

has the smallest possible dimension.

Conversion to state-space form is not uniquely defined in the SISO case. It is also not guaranteed to produce a minimal realization in the MIMO case. For more information, see Recommended Working Representation.

returns an explicit state-space realization `sys`

= ss(`ssSys`

,'explicit')*(E = I)* of the dynamic
system state-space model `ssSys`

. `ss`

returns an
error if `ssSys`

is improper. For more information on explicit
state-space realization, see State-Space Models.

### Input Arguments

`A`

— State matrix

`Nx`

-by-`Nx`

matrix

State matrix, specified as an `Nx`

-by-`Nx`

matrix where, `Nx`

is the number of states. This input sets the value
of property A.

`B`

— Input-to-state matrix

`Nx`

-by-`Nu`

matrix

Input-to-state matrix, specified as an
`Nx`

-by-`Nu`

matrix where, `Nx`

is the number of states and `Nu`

is the number of inputs. This input
sets the value of property B.

`C`

— State-to-output matrix

`Ny`

-by-`Nx`

matrix

State-to-output matrix, specified as an
`Ny`

-by-`Nx`

matrix where, `Nx`

is the number of states and `Ny`

is the number of outputs. This input
sets the value of property C.

`D`

— Feedthrough matrix

`Ny`

-by-`Nu`

matrix

Feedthrough matrix, specified as an `Ny`

-by-`Nu`

matrix where, `Ny`

is the number of outputs and `Nu`

is the number of inputs. This input sets the value of property D.

`ts`

— Sample time

scalar

Sample time, specified as a scalar. For more information, see Ts property.

`ltiSys`

— Dynamic system to convert to state-space form

dynamic system model | model array

Dynamic system to convert to state-space form, specified as a SISO or MIMO dynamic system model or array of dynamic system models. Dynamic systems that you can convert 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 state-space model 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`

,`ss`

converts the measured component of the identified model by default. (Using identified models requires System Identification Toolbox™ software.)

`component`

— Component of identified model

`'measured'`

(default) | `'noise'`

| `'augmented'`

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.

`ssSys`

— Dynamic system model to convert to minimal realization or explicit form

`ss`

model object

Dynamic system model to convert to minimal realization or explicit form, specified
as an `ss`

model object.

### Output Arguments

`sys`

— Output system model

`ss`

model object | `genss`

model object | `uss`

model object

Output system model, returned as:

A state-space (

`ss`

) model object, when the inputs`A`

,`B`

,`C`

and`D`

are numeric matrices or when converting from another model object type.A generalized state-space model (

`genss`

) object, when one or more of the matrices`A`

,`B`

,`C`

and`D`

includes tunable parameters, such as`realp`

parameters or generalized matrices (`genmat`

). For an example, see Create State-Space Model with Both Fixed and Tunable Parameters.An uncertain state-space model (

`uss`

) object, when one or more of the inputs`A`

,`B`

,`C`

and`D`

includes uncertain matrices. Using uncertain models requires Robust Control Toolbox software.

## Properties

`A`

— State matrix

`Nx`

-by-`Nx`

matrix

State matrix, specified as an `Nx`

-by-`Nx`

matrix
where `Nx`

is the number of states. The state-matrix can be represented
in many ways depending on the desired state-space model realization such as:

Model Canonical Form

Companion Canonical Form

Observable Canonical Form

Controllable Canonical Form

For more information, see State-Space Realizations.

`B`

— Input-to-state matrix

`Nx`

-by-`Nu`

matrix

Input-to-state matrix, specified as an
`Nx`

-by-`Nu`

matrix where `Nx`

is
the number of states and `Nu`

is the number of inputs.

`C`

— State-to-output matrix

`Ny`

-by-`Nx`

matrix

State-to-output matrix, specified as an
`Ny`

-by-`Nx`

matrix where `Nx`

is
the number of states and `Ny`

is the number of outputs.

`D`

— Feedthrough matrix

`Ny`

-by-`Nu`

matrix

Feedthrough matrix, specified as an `Ny`

-by-`Nu`

matrix where `Ny`

is the number of outputs and `Nu`

is
the number of inputs. `D`

is also called as the static gain matrix
which represents the ratio of the output to the input under steady state
condition.

`E`

— Matrix for implicit state-space models

`[]`

(default) | `Nx`

-by-`Nx`

matrix

Matrix for implicit or descriptor state-space models, specified as a
`Nx`

-by-`Nx`

matrix. `E`

is empty
by default, meaning that the state equation is explicit. To specify an implicit state
equation *E*
*dx*/*dt* = *Ax* +
*Bu*, set this property to a square matrix of the same size as
`A`

. See `dss`

for more information about creating
descriptor state-space models.

`Offsets`

— Model offsets

`[]`

(default) | structure

*Since R2024a*

Model offsets, specified as a structure with these fields.

Field | Description |
---|---|

`u` | Input offsets, specified as a vector of length equal to the number of inputs. |

`y` | Output offsets, specified as a vector of length equal to the number of outputs. |

`x` | State offsets, specified as a vector of length equal to the number of states. |

`dx` | State derivative offsets, specified as a vector of length equal to the number of states. |

For state-space model arrays, set `Offsets`

to a structure array
with the same dimension as the model array.

When you linearize the nonlinear model

$$\begin{array}{cc}\dot{x}=f(x,u),& y=g(x,u)\end{array}$$

around an operating point
(*x*_{0},*u*_{0}),
the resulting model is a state-space model with offsets:

$$\begin{array}{c}\dot{x}=\underset{{\delta}_{0}}{\underbrace{f({x}_{0},{u}_{0})}}+A(x-{x}_{0})+B(u-{u}_{0})\\ y=\underset{{y}_{0}}{\underbrace{g({x}_{0},{u}_{0})}}+C(x-{x}_{0})+D(u-{u}_{0}),\end{array}$$

where

$$\begin{array}{cccc}A=\frac{\partial f}{\partial x}({x}_{0},{u}_{0}),& B=\frac{\partial f}{\partial u}({x}_{0},{u}_{0}),& C=\frac{\partial g}{\partial x}({x}_{0},{u}_{0}),& D=\frac{\partial g}{\partial u}({x}_{0},{u}_{0}).\end{array}$$

For the linearization to be a good approximation of the nonlinear maps, it must
include the offsets *δ*_{0},
*x*_{0},
*u*_{0}, and
*y*_{0}. The `linearize`

(Simulink Control Design) command returns both *A*, *B*,
*C*, *D* and the offsets when using the
`StoreOffset`

option.

This property helps you manage linearization offsets and use them in operations such as response simulation, model interconnections, and model transformations.

`Scaled`

— Logical value indicating whether scaling is enabled or disabled

`0`

(default) | `1`

Logical value indicating whether scaling is enabled or disabled, specified as either
`0`

or `1`

.

When `Scaled`

is set to `0`

(disabled), then most
numerical algorithms acting on the state-space model `sys`

automatically rescale the state vector to improve numerical accuracy. You can prevent
such auto-scaling by setting `Scaled`

to `1`

(enabled).

For more information about scaling, see `prescale`

.

`StateName`

— State names

`' '`

(default) | character vector | cell array of character vectors

State names, specified as one of the following:

Character vector — For first-order models, for example,

`'velocity'`

Cell array of character vectors — For models with two or more states

`StateName`

is empty `' '`

for all states by
default.

`StatePath`

— State path

`' '`

(default) | character vector | cell array of character vectors

State path to facilitate state block path management in linearization, specified as one of the following:

Character vector — For first-order models

Cell array of character vectors — For models with two or more states

`StatePath`

is empty `' '`

for all states by
default.

`StateUnit`

— State units

`' '`

(default) | character vector | cell array of character vectors

State units, specified as one of the following:

Character vector — For first-order models, for example,

`'m/s'`

Cell array of character vectors — For models with two or more states

Use `StateUnit`

to keep track of the units of each state.
`StateUnit`

has no effect on system behavior.
`StateUnit`

is empty `' '`

for all states by
default.

`InternalDelay`

— Internal delays in the model

vector

Internal delays in the model, specified as a vector. Internal delays arise, for example, when closing feedback loops on systems with delays, or when connecting delayed systems in series or parallel. For more information about internal delays, see Closing Feedback Loops with Time Delays.

For continuous-time models, internal delays are expressed in the time unit specified
by the `TimeUnit`

property of the model. For discrete-time models,
internal delays are expressed as integer multiples of the sample time
`Ts`

. For example, `InternalDelay = 3`

means a delay
of three sampling periods.

You can modify the values of internal delays using the property
`InternalDelay`

. However, the number of entries in
`sys.InternalDelay`

cannot change, because it is a structural
property of the model.

`InputDelay`

— Input delay

`0`

(default) | scalar | `Nu`

-by-1 vector

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`

.

For more information, see Time Delays in Linear Systems.

`OutputDelay`

— Output delay

`0`

(default) | scalar | `Ny`

-by-1 vector

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`

.

For more information, see Time Delays in Linear Systems.

`Ts`

— Sample time

`0`

(default) | positive scalar | `-1`

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.

`TimeUnit`

— Time variable units

`'seconds'`

(default) | `'nanoseconds'`

| `'microseconds'`

| `'milliseconds'`

| `'minutes'`

| `'hours'`

| `'days'`

| `'weeks'`

| `'months'`

| `'years'`

| ...

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.

`InputName`

— Input channel names

`''`

(default) | character vector | cell array of character vectors

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 the
following:

`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.

`InputUnit`

— Input channel units

`''`

(default) | character vector | cell array of character vectors

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.

`InputGroup`

— Input channel groups

structure

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, enter the following to create input
groups named `controls`

and `noise`

that include input
channels `1`

and `2`

, and `3`

and
`5`

, respectively.

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

You can then extract the subsystem from the `controls`

inputs to all outputs
using the following.

`sys(:,'controls')`

By default, `InputGroup`

is a structure with no fields.

`OutputName`

— Output channel names

`''`

(default) | character vector | cell array of character vectors

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 the
following.

`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.

`OutputUnit`

— Output channel units

`''`

(default) | character vector | cell array of character vectors

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.

`OutputGroup`

— Output channel groups

structure

Output channel groups, specified as a structure. Use `OutputGroup`

to 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, create output groups named
`temperature`

and `measurement`

that include
output channels `1`

, and `3`

and `5`

,
respectively.

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

You can then extract the subsystem from all inputs to the `measurement`

outputs using the following.

`sys('measurement',:)`

By default, `OutputGroup`

is a structure with no fields.

`Name`

— System name

`''`

(default) | character vector

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

.

`Notes`

— User-specified text

`{}`

(default) | character vector | cell array of character vectors

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'`

.

`UserData`

— User-specified data

`[]`

(default) | any MATLAB data type

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

`SamplingGrid`

— Sampling grid for model arrays

structure array

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
`ss`

model objects. In general, any function applicable to Dynamic System Models is
applicable to an `ss`

object.

### Linear Analysis

`step` | Step response of dynamic system |

`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 |

### Stability Analysis

### Model Transformation

### Model Interconnection

## Examples

### SISO State-Space Model

Create the SISO state-space model defined by the following state-space matrices:

$$A=\left[\begin{array}{cc}-1.5& -2\\ 1& 0\end{array}\right]\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}B=\left[\begin{array}{c}0.5\\ 0\end{array}\right]\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}C=\left[\begin{array}{cc}0& 1\end{array}\right]\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}D=0$$

Specify the A, B, C and D matrices, and create the state-space model.

A = [-1.5,-2;1,0]; B = [0.5;0]; C = [0,1]; D = 0; sys = ss(A,B,C,D)

sys = A = x1 x2 x1 -1.5 -2 x2 1 0 B = u1 x1 0.5 x2 0 C = x1 x2 y1 0 1 D = u1 y1 0 Continuous-time state-space model.

### Create Discrete-Time State-Space Model

Create a state-space model with a sample time of 0.25 seconds and the following state-space matrices:

$$A=\left[\begin{array}{cc}0& 1\\ -5& -2\end{array}\right]\phantom{\rule{1em}{0ex}}B=\left[\begin{array}{c}0\\ 3\end{array}\right]\phantom{\rule{1em}{0ex}}C=[\phantom{\rule{0.1em}{0ex}}\begin{array}{cc}0& 1\end{array}]\phantom{\rule{1em}{0ex}}D=[\phantom{\rule{0.1em}{0ex}}0\phantom{\rule{0.1em}{0ex}}]$$

Specify the state-space matrices.

A = [0 1;-5 -2]; B = [0;3]; C = [0 1]; D = 0;

Specify the sample time.

Ts = 0.25;

Create the state-space model.

sys = ss(A,B,C,D,Ts);

### Continuous-Time MIMO State-Space Model

For this example, consider a cube rotating about its corner with inertia tensor `J`

and a damping force `F`

of 0.2 magnitude. The input to the system is the driving torque while the angular velocities are the outputs. The state-space matrices for the cube are:

$$\begin{array}{l}A=-{J}^{-1}F,\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}B={J}^{-1},\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}C=I,\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}D=0,\\ where,\phantom{\rule{0.2777777777777778em}{0ex}}J=\left[\begin{array}{ccc}8& -3& -3\\ -3& 8& -3\\ -3& -3& 8\end{array}\right]\phantom{\rule{0.2777777777777778em}{0ex}}and\phantom{\rule{0.2777777777777778em}{0ex}}F=\left[\begin{array}{ccc}0.2& 0& 0\\ 0& 0.2& 0\\ 0& 0& 0.2\end{array}\right]\end{array}$$

Specify the `A`

, `B`

, `C`

and `D`

matrices, and create the continuous-time state-space model.

J = [8 -3 -3; -3 8 -3; -3 -3 8]; F = 0.2*eye(3); A = -J\F; B = inv(J); C = eye(3); D = 0; sys = ss(A,B,C,D)

sys = A = x1 x2 x3 x1 -0.04545 -0.02727 -0.02727 x2 -0.02727 -0.04545 -0.02727 x3 -0.02727 -0.02727 -0.04545 B = u1 u2 u3 x1 0.2273 0.1364 0.1364 x2 0.1364 0.2273 0.1364 x3 0.1364 0.1364 0.2273 C = x1 x2 x3 y1 1 0 0 y2 0 1 0 y3 0 0 1 D = u1 u2 u3 y1 0 0 0 y2 0 0 0 y3 0 0 0 Continuous-time state-space model.

`sys`

is MIMO since the system contains 3 inputs and 3 outputs observed from matrices `C`

and `D`

. For more information on MIMO state-space models, see MIMO State-Space Models.

### Discrete-Time MIMO State-Space Model

Create a state-space model using the following discrete-time, multi-input, multi-output state matrices with sample time `ts = 0.2`

seconds:

$$A=\left[\begin{array}{cc}-7& 0\\ 0& -10\end{array}\right]\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}B=\left[\begin{array}{cc}5& 0\\ 0& 2\end{array}\right]\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}C=\left[\begin{array}{cc}1& -4\\ -4& 0.5\end{array}\right]\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}D=\left[\begin{array}{cc}0& -2\\ 2& 0\end{array}\right]$$

Specify the state-space matrices and create the discrete-time MIMO state-space model.

A = [-7,0;0,-10]; B = [5,0;0,2]; C = [1,-4;-4,0.5]; D = [0,-2;2,0]; ts = 0.2; sys = ss(A,B,C,D,ts)

sys = A = x1 x2 x1 -7 0 x2 0 -10 B = u1 u2 x1 5 0 x2 0 2 C = x1 x2 y1 1 -4 y2 -4 0.5 D = u1 u2 y1 0 -2 y2 2 0 Sample time: 0.2 seconds Discrete-time state-space model.

### Specify State and Input Names for State-Space Model

Create state-space matrices and specify sample time.

A = [-0.2516 -0.1684;2.784 0.3549]; B = [0;3]; C = [0 1]; D = 0; Ts = 0.05;

Create the state-space model, specifying the state and input names using name-value pairs.

sys = ss(A,B,C,D,Ts,'StateName',{'Position' 'Velocity'},... 'InputName','Force');

The number of state and input names must be consistent with the dimensions of `A`

, `B`

, `C`

, and `D`

.

Naming the inputs and outputs can be useful when dealing with response plots for MIMO systems.

step(sys)

Notice the input name `Force`

in the title of the step response plot.

### State-Space Model with Inherited Properties

For this example, create a state-space model with the same time and input unit properties inherited from another state-space model. Consider the following state-space models:

$$\begin{array}{l}{A}_{1}=\left[\begin{array}{cc}-1.5& -2\\ 1& 0\end{array}\right]\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}{B}_{1}=\left[\begin{array}{c}0.5\\ 0\end{array}\right]\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}{C}_{1}=\left[\begin{array}{cc}0& 1\end{array}\right]\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}{D}_{1}=5\\ {A}_{2}=\left[\begin{array}{cc}7& -1\\ 0& 2\end{array}\right]\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}{B}_{2}=\left[\begin{array}{c}0.85\\ 2\end{array}\right]\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}{C}_{2}=\left[\begin{array}{cc}10& 14\end{array}\right]\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}{D}_{2}=2\end{array}$$

First, create a state-space model `sys1`

with the `TimeUnit`

and `InputUnit`

property set to '`minutes`

'.

A1 = [-1.5,-2;1,0]; B1 = [0.5;0]; C1 = [0,1]; D1 = 5; sys1 = ss(A1,B1,C1,D1,'TimeUnit','minutes','InputUnit','minutes');

Verify that the time and input unit properties of `sys1`

are set to '`minutes`

'.

propValues1 = [sys1.TimeUnit,sys1.InputUnit]

`propValues1 = `*1x2 cell*
{'minutes'} {'minutes'}

Create the second state-space model with properties inherited from `sys1`

.

A2 = [7,-1;0,2]; B2 = [0.85;2]; C2 = [10,14]; D2 = 2; sys2 = ss(A2,B2,C2,D2,sys1);

Verify that the time and input units of `sys2`

have been inherited from `sys1`

.

propValues2 = [sys2.TimeUnit,sys2.InputUnit]

`propValues2 = `*1x2 cell*
{'minutes'} {'minutes'}

### MIMO Static Gain State-Space Model

In this example, you will create a static gain MIMO state-space model.

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

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

Specify the gain matrix and create the static gain state-space model.

D = [2,4;3,5]; sys1 = ss(D)

sys1 = D = u1 u2 y1 2 4 y2 3 5 Static gain.

### Convert Transfer Function to State-Space Model

Compute the state-space model of the following transfer function:

$$H\left(s\right)=\left[\begin{array}{c}\frac{s+1}{{s}^{3}+3{s}^{2}+3s+2}\\ \frac{{s}^{2}+3}{{s}^{2}+s+1}\end{array}\right]$$

Create the transfer function model.

H = [tf([1 1],[1 3 3 2]) ; tf([1 0 3],[1 1 1])];

Convert this model to a state-space model.

sys = ss(H);

Examine the size of the state-space model.

size(sys)

State-space model with 2 outputs, 1 inputs, and 5 states.

The number of states is equal to the cumulative order of the SISO entries in *H*(*s*).

To obtain a minimal realization of *H*(*s*), enter

```
sys = ss(H,'minimal');
size(sys)
```

State-space model with 2 outputs, 1 inputs, and 3 states.

The resulting model has an order of three, which is the minimum number of states needed to represent *H*(*s*). To see this number of states, refactor *H*(*s*) as the product of a first-order system and a second-order system.

$$H(s)=\left[\begin{array}{cc}\frac{1}{s+2}& 0\\ 0& 1\end{array}\right]\left[\begin{array}{c}\frac{s+1}{{s}^{2}+s+1}\\ \frac{{s}^{2}+3}{{s}^{2}+s+1}\end{array}\right]$$

### Extract State-Space Models from Identified Model

For this example, extract the measured and noise components of an identified polynomial model into two separate state-space models.

Load the Box-Jenkins polynomial model `ltiSys`

in `identifiedModel.mat`

.

load('identifiedModel.mat','ltiSys');

`ltiSys`

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

Extract the measured and noise components as state-space models.

`sysMeas = ss(ltiSys,'measured') `

sysMeas = A = x1 x2 x1 1.575 -0.6115 x2 1 0 B = u1 x1 0.5 x2 0 C = x1 x2 y1 -0.2851 0.3916 D = u1 y1 0 Input delays (sampling periods): 2 Sample time: 0.04 seconds Discrete-time state-space model.

`sysNoise = ss(ltiSys,'noise')`

sysNoise = A = x1 x2 x3 x1 1.026 -0.26 0.3899 x2 1 0 0 x3 0 0.5 0 B = v@y1 x1 0.25 x2 0 x3 0 C = x1 x2 x3 y1 0.319 -0.04738 0.07106 D = v@y1 y1 0.04556 Input groups: Name Channels Noise 1 Sample time: 0.04 seconds Discrete-time state-space model.

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

### Explicit Realization of Descriptor State-Space Model

Create a descriptor state-space model (*E* ≠ *I*).

a = [2 -4; 4 2]; b = [-1; 0.5]; c = [-0.5, -2]; d = [-1]; e = [1 0; -3 0.5]; sysd = dss(a,b,c,d,e);

Compute an explicit realization of the system (*E* = *I*).

`syse = ss(sysd,'explicit')`

syse = A = x1 x2 x1 2 -4 x2 20 -20 B = u1 x1 -1 x2 -5 C = x1 x2 y1 -0.5 -2 D = u1 y1 -1 Continuous-time state-space model.

Confirm that the descriptor and explicit realizations have equivalent dynamics.

`bodeplot(sysd,syse,'g--')`

### Create State-Space Model with Both Fixed and Tunable Parameters

This example shows how to create a state-space `genss`

model having both fixed and tunable parameters.

$$A=\left[\begin{array}{cc}1& a+b\\ 0& ab\end{array}\right],\phantom{\rule{1em}{0ex}}B=\left[\begin{array}{c}-3.0\\ 1.5\end{array}\right],\phantom{\rule{1em}{0ex}}C=\left[\begin{array}{cc}0.3& 0\end{array}\right],\phantom{\rule{1em}{0ex}}D=0,$$

where *a* and *b* are tunable parameters, whose initial values are `-1`

and `3`

, respectively.

Create the tunable parameters using `realp`

.

a = realp('a',-1); b = realp('b',3);

Define a generalized matrix using algebraic expressions of `a`

and `b`

.

A = [1 a+b;0 a*b];

`A`

is a generalized matrix whose `Blocks`

property contains `a`

and `b`

. The initial value of `A`

is `[1 2;0 -3]`

, from the initial values of `a`

and `b`

.

Create the fixed-value state-space matrices.

B = [-3.0;1.5]; C = [0.3 0]; D = 0;

Use `ss`

to create the state-space model.

sys = ss(A,B,C,D)

Generalized continuous-time state-space model with 1 outputs, 1 inputs, 2 states, and the following blocks: a: Scalar parameter, 2 occurrences. b: Scalar parameter, 2 occurrences. Type "ss(sys)" to see the current value and "sys.Blocks" to interact with the blocks.

`sys`

is a generalized LTI model (`genss`

) with tunable parameters `a`

and `b`

.

### State-Space Model with Input and Output Delay

For this example, consider a SISO state-space model defined by the following state-space matrices:

$$A=\left[\begin{array}{cc}-1.5& -2\\ 1& 0\end{array}\right]\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}B=\left[\begin{array}{c}0.5\\ 0\end{array}\right]\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}C=\left[\begin{array}{cc}0& 1\end{array}\right]\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}\phantom{\rule{0.2777777777777778em}{0ex}}D=0$$

Considering an input delay of 0.5 seconds and an output delay of 2.5 seconds, create a state-space model object to represent the A, B, C and D matrices.

A = [-1.5,-2;1,0]; B = [0.5;0]; C = [0,1]; D = 0; sys = ss(A,B,C,D,'InputDelay',0.5,'OutputDelay',2.5)

sys = A = x1 x2 x1 -1.5 -2 x2 1 0 B = u1 x1 0.5 x2 0 C = x1 x2 y1 0 1 D = u1 y1 0 Input delays (seconds): 0.5 Output delays (seconds): 2.5 Continuous-time state-space model.

You can also use the `get`

command to display all the properties of a MATLAB object.

get(sys)

A: [2x2 double] B: [2x1 double] C: [0 1] D: 0 E: [] Offsets: [] Scaled: 0 StateName: {2x1 cell} StatePath: {2x1 cell} StateUnit: {2x1 cell} InternalDelay: [0x1 double] InputDelay: 0.5000 OutputDelay: 2.5000 InputName: {''} InputUnit: {''} InputGroup: [1x1 struct] OutputName: {''} OutputUnit: {''} OutputGroup: [1x1 struct] Notes: [0x1 string] UserData: [] Name: '' Ts: 0 TimeUnit: 'seconds' SamplingGrid: [1x1 struct]

For more information on specifying time delay for an LTI model, see Specifying Time Delays.

### Stability Analysis of State-Space Systems

For this example, consider a state-space System object™ that represents the following state matrices:

$$A=\left[\begin{array}{ccc}-1.2& -1.6& 0\\ 1& 0& 0\\ 0& 1& 0\end{array}\right],\phantom{\rule{1em}{0ex}}B=\left[\begin{array}{c}1\\ 0\\ 0\end{array}\right],\phantom{\rule{1em}{0ex}}C=\left[\begin{array}{ccc}0& 0.5& 1.3\end{array}\right],\phantom{\rule{1em}{0ex}}D=0,$$

Create a state-space object `sys`

using the `ss`

command.

A = [-1.2,-1.6,0;1,0,0;0,1,0]; B = [1;0;0]; C = [0,0.5,1.3]; D = 0; sys = ss(A,B,C,D);

Next, compute the closed-loop state-space model for a unit negative gain and find the poles of the closed-loop state-space System object `sysFeedback`

.

sysFeedback = feedback(sys,1); P = pole(sysFeedback)

`P = `*3×1 complex*
-0.2305 + 1.3062i
-0.2305 - 1.3062i
-0.7389 + 0.0000i

The feedback loop for unit gain is stable since all poles have negative real parts. Checking the closed-loop poles provides a binary assessment of stability. In practice, it is more useful to know how robust (or fragile) stability is. One indication of robustness is how much the loop gain can change before stability is lost. You can use the root locus plot to estimate the range of `k`

values for which the loop is stable.

rlocus(sys)

Changes in the loop gain are only one aspect of robust stability. In general, imperfect plant modeling means that both gain and phase are not known exactly. Since modeling errors have the most detrimental effect near the gain crossover frequency (frequency where open-loop gain is 0dB), it also matters how much phase variation can be tolerated at this frequency.

You can display the gain and phase margins on a Bode plot as follows.

bode(sys) grid

For a more detailed example, see Assessing Gain and Phase Margins.

### Control Design Using State-Space Models

For this example, design a 2-DOF PID controller with a target bandwidth of 0.75 rad/s for a system represented by the following matrices:

$$A=\left[\begin{array}{cc}-0.5& -0.1\\ 1& 0\end{array}\right],\phantom{\rule{1em}{0ex}}B=\left[\begin{array}{c}1\\ 0\end{array}\right],\phantom{\rule{1em}{0ex}}C=\left[\begin{array}{cc}0& 1\end{array}\right],\phantom{\rule{1em}{0ex}}D=0.$$

Create a state-space object `sys`

using the `ss`

command.

A = [-0.5,-0.1;1,0]; B = [1;0]; C = [0,1]; D = 0; sys = ss(A,B,C,D)

sys = A = x1 x2 x1 -0.5 -0.1 x2 1 0 B = u1 x1 1 x2 0 C = x1 x2 y1 0 1 D = u1 y1 0 Continuous-time state-space model.

Using the target bandwidth, use `pidtune`

to generate a 2-DOF controller.

```
wc = 0.75;
C2 = pidtune(sys,'PID2',wc)
```

C2 = 1 u = Kp (b*r-y) + Ki --- (r-y) + Kd*s (c*r-y) s with Kp = 0.513, Ki = 0.0975, Kd = 0.577, b = 0.344, c = 0 Continuous-time 2-DOF PID controller in parallel form.

Using the type `'PID2'`

causes `pidtune`

to generate a 2-DOF controller, represented as a `pid2`

object. The display confirms this result. The display also shows that `pidtune`

tunes all controller coefficients, including the setpoint weights `b`

and `c`

, to balance performance and robustness.

For interactive PID tuning in the Live Editor, see the Tune PID Controller Live Editor task. This task lets you interactively design a PID controller and automatically generates MATLAB code for your live script.

For interactive PID tuning in a standalone app, use PID Tuner. See PID Controller Design for Fast Reference Tracking for an example of designing a controller using the app.

### Connect Specific Inputs and Outputs of State-Space Models in a Feedback Loop

Consider a state-space plant `G`

with five inputs and four outputs and a state-space feedback controller `K`

with three inputs and two outputs. The outputs 1, 3, and 4 of the plant `G`

must be connected the controller `K`

inputs, and the controller outputs to inputs 4 and 2 of the plant.

For this example, consider two continuous-time state-space models for both `G`

and `K`

represented by the following set of matrices:

$${A}_{G}=\left[\begin{array}{ccc}-3& 0.4& 0.3\\ -0.5& -2.8& -0.8\\ 0.2& 0.8& -3\end{array}\right],\phantom{\rule{1em}{0ex}}{B}_{G}=\left[\begin{array}{ccccc}0.4& 0& 0.3& 0.2& 0\\ -0.2& -1& 0.1& -0.9& -0.5\\ 0.6& 0.9& 0.5& 0.2& 0\end{array}\right],\phantom{\rule{1em}{0ex}}{C}_{G}=\left[\begin{array}{ccc}0& -0.1& -1\\ 0& -0.2& 1.6\\ -0.7& 1.5& 1.2\\ -1.4& -0.2& 0\end{array}\right],\phantom{\rule{1em}{0ex}}{D}_{G}=\left[\begin{array}{ccccc}0& 0& 0& 0& -1\\ 0& 0.4& -0.7& 0& 0.9\\ 0& 0.3& 0& 0& 0\\ 0.2& 0& 0& 0& 0\end{array}\right]$$

$${A}_{K}=\left[\begin{array}{ccc}-0.2& 2.1& 0.7\\ -2.2& -0.1& -2.2\\ -0.4& 2.3& -0.2\end{array}\right],\phantom{\rule{1em}{0ex}}{B}_{K}=\left[\begin{array}{ccc}-0.1& -2.1& -0.3\\ -0.1& 0& 0.6\\ 1& 0& 0.8\end{array}\right],\phantom{\rule{1em}{0ex}}{C}_{K}=\left[\begin{array}{ccc}-1& 0& 0\\ -0.4& -0.2& 0.3\end{array}\right],\phantom{\rule{1em}{0ex}}{D}_{K}=\left[\begin{array}{ccc}0& 0& 0\\ 0& 0& -1.2\end{array}\right]$$

AG = [-3,0.4,0.3;-0.5,-2.8,-0.8;0.2,0.8,-3]; BG = [0.4,0,0.3,0.2,0;-0.2,-1,0.1,-0.9,-0.5;0.6,0.9,0.5,0.2,0]; CG = [0,-0.1,-1;0,-0.2,1.6;-0.7,1.5,1.2;-1.4,-0.2,0]; DG = [0,0,0,0,-1;0,0.4,-0.7,0,0.9;0,0.3,0,0,0;0.2,0,0,0,0]; sysG = ss(AG,BG,CG,DG)

sysG = A = x1 x2 x3 x1 -3 0.4 0.3 x2 -0.5 -2.8 -0.8 x3 0.2 0.8 -3 B = u1 u2 u3 u4 u5 x1 0.4 0 0.3 0.2 0 x2 -0.2 -1 0.1 -0.9 -0.5 x3 0.6 0.9 0.5 0.2 0 C = x1 x2 x3 y1 0 -0.1 -1 y2 0 -0.2 1.6 y3 -0.7 1.5 1.2 y4 -1.4 -0.2 0 D = u1 u2 u3 u4 u5 y1 0 0 0 0 -1 y2 0 0.4 -0.7 0 0.9 y3 0 0.3 0 0 0 y4 0.2 0 0 0 0 Continuous-time state-space model.

AK = [-0.2,2.1,0.7;-2.2,-0.1,-2.2;-0.4,2.3,-0.2]; BK = [-0.1,-2.1,-0.3;-0.1,0,0.6;1,0,0.8]; CK = [-1,0,0;-0.4,-0.2,0.3]; DK = [0,0,0;0,0,-1.2]; sysK = ss(AK,BK,CK,DK)

sysK = A = x1 x2 x3 x1 -0.2 2.1 0.7 x2 -2.2 -0.1 -2.2 x3 -0.4 2.3 -0.2 B = u1 u2 u3 x1 -0.1 -2.1 -0.3 x2 -0.1 0 0.6 x3 1 0 0.8 C = x1 x2 x3 y1 -1 0 0 y2 -0.4 -0.2 0.3 D = u1 u2 u3 y1 0 0 0 y2 0 0 -1.2 Continuous-time state-space model.

Define the `feedout`

and `feedin`

vectors based on the inputs and outputs to be connected in a feedback loop.

feedin = [4 2]; feedout = [1 3 4]; sys = feedback(sysG,sysK,feedin,feedout,-1)

sys = A = x1 x2 x3 x4 x5 x6 x1 -3 0.4 0.3 0.2 0 0 x2 1.18 -2.56 -0.8 -1.3 -0.2 0.3 x3 -1.312 0.584 -3 0.56 0.18 -0.27 x4 2.948 -2.929 -2.42 -0.452 1.974 0.889 x5 -0.84 -0.11 0.1 -2.2 -0.1 -2.2 x6 -1.12 -0.26 -1 -0.4 2.3 -0.2 B = u1 u2 u3 u4 u5 x1 0.4 0 0.3 0.2 0 x2 -0.44 -1 0.1 -0.9 -0.5 x3 0.816 0.9 0.5 0.2 0 x4 -0.2112 -0.63 0 0 0.1 x5 0.12 0 0 0 0.1 x6 0.16 0 0 0 -1 C = x1 x2 x3 x4 x5 x6 y1 0 -0.1 -1 0 0 0 y2 -0.672 -0.296 1.6 0.16 0.08 -0.12 y3 -1.204 1.428 1.2 0.12 0.06 -0.09 y4 -1.4 -0.2 0 0 0 0 D = u1 u2 u3 u4 u5 y1 0 0 0 0 -1 y2 0.096 0.4 -0.7 0 0.9 y3 0.072 0.3 0 0 0 y4 0.2 0 0 0 0 Continuous-time state-space model.

size(sys)

State-space model with 4 outputs, 5 inputs, and 6 states.

`sys`

is the resultant closed loop state-space model obtained by connecting the specified inputs and outputs of `G`

and `K`

.

### Create State-Space Model with Offsets

*Since R2024a*

This example shows how to linearize a Simulink® model and store the linearization offsets in the `Offsets`

property of the `ss`

model object.

Open the Simulink model.

```
mdl = 'watertankNLModel';
open_system(mdl)
```

Specify the initial condition for water height.

h0 = 10;

Specify model linear analysis points.

io(1) = linio('watertankNLModel/Step',1,'input'); io(2) = linio('watertankNLModel/H',1,'output');

Simulate the model and extract operating points at time snapshots.

tlin = [0 15 30]; op = findop(mdl,tlin);

Compute the linearization result along with offsets.

```
options = linearizeOptions('StoreOffsets',true);
[linsys,~,info] = linearize(mdl,io,op,options);
```

The function returns an array of state-space models `linsys`

and their corresponding linearization offsets in `info.Offsets`

.

The `Offsets`

property of the `ss`

model object requires a structure with fields `u`

, `y`

, `x`

, and `dx`

. You can use the `info.Offsets`

output from `linearize`

to set these offsets directly.

linsys.Offsets = info.Offsets; linsys.Offsets

`ans=`*3×1 struct array with fields:*
dx
x
u
y

## Version History

**Introduced before R2006a**

### R2024a: Create state-space models with offsets

Use the new `Offsets`

property to store model offsets. Offsets
usually arise when linearizing nonlinear dynamics at some operating conditions. This
property helps you manage linearization offsets and use them in operations such as response
simulation, model interconnections, and model transformations.

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