StateSpace
Implement linear statespace system
Libraries:
Simulink /
Continuous
Description
The StateSpace block implements a system whose behavior you define as
$$\begin{array}{l}\dot{x}=Ax+Bu\\ y=Cx+Du\\ {x}_{t={t}_{0}}={x}_{0},\end{array}$$
where x is the state vector, u is the input vector, y is the output vector, and x0 is the initial condition of the state vector. The A, B, C, and D matrices can be specified as either sparse matrices or dense matrices. The matrix coefficients must have these characteristics:
A must be an nbyn matrix, where n is the number of states.
B must be an nbym matrix, where m is the number of inputs.
C must be an rbyn matrix, where r is the number of outputs.
D must be an rbym matrix.
In general, the block has one input port and one output port. The number of rows in C or D matrix is the same as the width of the output port. The number of columns in the B or D matrix are the same as the width of the input port. If you want to model an autonomous linear system with no inputs, set the B and D matrices to empty. In this case, the block acts as a source block with no input port and one output port, and implements the following system:
$$\begin{array}{l}\dot{x}=Ax\\ y=Cx\\ {x}_{t={t}_{0}}={x}_{0}.\end{array}$$
Simulink^{®} software converts a matrix containing zeros to a sparse matrix for efficient multiplication.
Examples
Ports
Input
Port_1 — Input signal
scalar  vector
Realvalued input vector of type double
, where the
width equals the number of columns in the B and
D matrices. For more information, see Description.
Data Types: double
Output
Port_1 — Output vector
scalar  vector
Realvalued output vector of data type double
, with
width equal to the number of rows in the C and
D matrices. For more information, see Description.
Data Types: double
Parameters
A — Matrix coefficient, A
1
(default)  scalar  vector  matrix  sparse matrix
Specify the matrix coefficient A
, as a realvalued
nbyn matrix, where
n is the number of states. For more information on
the matrix coefficients, see Description.
Programmatic Use
Block Parameter:
A 
Type: character vector, string 
Values: scalar  vector  matrix  sparse matrix 
Default:
'1' 
B — Matrix coefficient, B
1
(default)  scalar  vector  matrix  sparse matrix
Specify the matrix coefficient B
, as a realvalued
nbym matrix, where
n is the number of states and m is
the number of inputs. For more information on the matrix coefficients, see
Description.
Programmatic Use
Block Parameter:
B 
Type: character vector, string 
Values: scalar  vector  matrix  sparse matrix 
Default:
'1' 
C — Matrix coefficient, C
1
(default)  scalar  vector  matrix  sparse matrix
Specify the matrix coefficient C as a realvalued rbyn matrix, where r is the number of outputs and n is the number of states. For more information on the matrix coefficients, see Description.
Programmatic Use
Block Parameter:
C 
Type: character vector, string 
Values: scalar  vector  matrix  sparse matrix 
Default:
'1' 
D — Matrix coefficient, D
1
(default)  scalar  vector  matrix  sparse matrix
Specify the matrix coefficient D as a realvalued rbym matrix, where r is the number of outputs and m is the number of inputs. For more information on the matrix coefficients, see Description.
Programmatic Use
Block Parameter:
D 
Type: character vector, string 
Values: scalar  vector  matrix  sparse matrix 
Default:
'1' 
Initial conditions — Initial state vector
0
(default)  scalar  vector
Specify the initial state vector.
Limitations
The initial conditions of this block cannot be inf
or NaN
.
Programmatic Use
Block Parameter:
X0

Type: character vector, string 
Values: scalar  vector 
Default:
'0' 
Parameter tunability — Choose tunable representation of block parameters
Auto
(default)  Optimized
 Unconstrained
Tunability level of the statespace matrices (A,B,C, and D ) for accelerated simulation
modes and deployed simulations using the Simulink
Compiler™. When set to Auto
, Simulink chooses the appropriate parameter tunability level.
For sparse matrix coefficients, set the parameter to
Optimized
to allow tunability of nonzero
elements while keeping the pattern and number of nonzero elements constant.
Set this parameter to Unconstrained
to allow all
elements to be tunable, so long as the number of nonzero elements is kept
constant, that is, you can change the pattern of the sparse matrix.
For dense matrix coefficients, select Optimized
to allow
tunability of all matrix elements, provided the number of nonzero elements
initially specified in the matrix is kept constant. Set this parameter to
Unconstrained
to allow full tunability of all
matrix elements.
Note
To tune the D matrix of the block when D = 0, you must enable the Allow nonzero values for D matrix initially specified as zero parameter.
Programmatic Use
Block Parameter:
ParameterTunability

Type: character vector  string 
Values:
'Auto'  'Optimized' 
'Unconstrained' 
Default:
'Auto' 
Allow nonzero values for D matrix initially specified as zero — Allow tunability of D matrix when D = 0
off
(default)  on
Enable this parameter to support tunability of D even when D = 0.
Note
Enabling this parameter enables direct feedthrough for the StateSpace block.
Programmatic Use
Block Parameter:
AllowTunableDMatrix

Type: character vector  string 
Values:
'off'  'on' 
Default:
'off' 
Absolute tolerance — Absolute tolerance for computing block states
auto
(default)  scalar  vector
Variablestep solvers use absolute and relative tolerances when choosing the step size to determine whether the error in state calculations is acceptable.
To inherit the absolute tolerance from the Absolute tolerance configuration parameter, specify this parameter
value as auto
or 1
.
To specify an absolute tolerance for this block that overrides the value specified for the Absolute tolerance configuration parameter:
Enter a real, positive scalar value to use to compute all block states.
Enter a real vector with dimensions that match the dimensions of the continuous states for the block.
Programmatic Use
Block Parameter: AbsoluteTolerance 
Type: string  character vector 
Values:
'auto'  '1'  positive, real scalar 
vector of positive, real scalars 
Default: 'auto' 
State Name (e.g., 'position') — Option to assign unique names to states
' '
(default)  character vector  cell array of character vectors  MATLAB^{®} variable  ...
Use this parameter to optionally assign names to the states of this block. The names you assign apply only to the states of this block.
To use default state names, leave this field blank (
''
).To assign a single name to a single state, enter the name between quotes. For example, to name a single state
position
, enter"position"
.To assign names to multiple states, specify this parameter value as a cell array of character vectors. Each name in the cell array must be unique. For example, to assign the names
a
,b
, andc
, enter{'a','b','c'}
.To specify the names using a MATLAB variable, enter the name of the variable without quotes. For example, to use the variable
names
to specify the state names, enternames
.
You can specify a number of names that is less than the number of states in the block. In this case, the state names are used for multiple states, and the number of states must divide evenly into the number of state names. For example, when you specify two names for a block that has four states, the first name is used for the first two states, and the second name is used for the last two states.
Programmatic Use
Block Parameter:
ContinuousStateAttributes 
Type: string  character vector 
Values:
' '  userdefined 
Default:
' ' 
Block Characteristics
Data Types 

Direct Feedthrough 

Multidimensional Signals 

VariableSize Signals 

ZeroCrossing Detection 

Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.
Not recommended for productionquality code. Relates to resource limits and restrictions on speed and memory often found in embedded systems. The code generated can contain dynamic allocation and freeing of memory, recursion, additional memory overhead, and widelyvarying execution times. While the code is functionally valid and generally acceptable in resourcerich environments, smaller embedded targets often cannot support such code.
In general, consider using the Simulink Model Discretizer to map continuous blocks into discrete equivalents that support production code generation. To start the Model Discretizer, in the Simulink Editor, on the Apps tab, under Apps, under Control Systems, click Model Discretizer. One exception is the SecondOrder Integrator block because, for this block, the Model Discretizer produces an approximate discretization.
Version History
Introduced before R2006a
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