State-Space

Implement linear state-space system

• Library:

• Simulink / Continuous

• 

Description

The State-Space 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 n-by-n matrix, where n is the number of states.

• B must be an n-by-m matrix, where m is the number of inputs.

• C must be an r-by-n matrix, where r is the number of outputs.

• D must be an r-by-m 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.

Ports

Input

Port_1 — Input signal

scalar | vector

Real-valued 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

Real-valued 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 real-valued n-by-n 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 real-valued n-by-m 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 real-valued r-by-n 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 real-valued r-by-m 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 state-space 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 non-zero elements while keeping the pattern and number of non-zero elements constant. Set this parameter to Unconstrained to allow all elements to be tunable, so long as the number of non-zero 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 non-zero 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 non-zero 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 non-zero 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 State-Space 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

Absolute tolerance for computing block states, specified as a positive, real-valued, scalar or vector. To inherit the absolute tolerance from the Configuration Parameters, specify auto or -1.

• If you enter a real scalar, then that value overrides the absolute tolerance in the Configuration Parameters dialog box for computing all block states.

• If you enter a real vector, then the dimension of that vector must match the dimension of the continuous states in the block. These values override the absolute tolerance in the Configuration Parameters dialog box.

• If you enter auto or –1, then Simulink uses the absolute tolerance value in the Configuration Parameters dialog box (see Solver Pane) to compute block states.

Programmatic Use

Block Parameter: AbsoluteTolerance

Type: character vector, string

Values: 'auto' | '-1' | any positive real-valued scalar or vector

Default: 'auto'

State Name (e.g., 'position') — Assign unique name to each state

' ' (default) | 'position' | {'a', 'b', 'c'} | a | ...

Assign a unique name to each state. If this field is blank (' '), no name assignment occurs.

• To assign a name to a single state, enter the name between quotes, for example, 'position'.

• To assign names to multiple states, enter a comma-delimited list surrounded by braces, for example, {'a', 'b', 'c'}. Each name must be unique.

• To assign state names with a variable in the MATLAB^® workspace, enter the variable without quotes. A variable can be a character vector, string, cell array, or structure.

Limitations

• The state names apply only to the selected block.

• The number of states must divide evenly among the number of state names.

• You can specify fewer names than states, but you cannot specify more names than states.

  For example, you can specify two names in a system with four states. The first name applies to the first two states and the second name to the last two states.

Programmatic Use

Block Parameter: ContinuousStateAttributes

Type: character vector, string

Values: ' ' | user-defined

Default: ' '

Model Examples

Double Spring Mass System

Model a double spring-mass-damper system with a periodically varying forcing function. Associated with the example is an animation function that will automatically open a figure window and display to it. In this system, the only sensor is attached to the mass on the left, and the actuator is attached to the mass on the left. State estimation and LQR control are used.

Open Model

Optical Sensor Image Generation

Generate a movie with 64 frames and a frame size of 64 by 64 pixels (at 10 frames per second). The movie contains a simulation of a moving target that is moving through a structured background that is itself moving. A jitter motion caused by random vibration is also generated (in a Simulink® model called "aero_vibrati") and the jitter motion is added into the overall sensor motion. Finally, the image is blurred through a Gaussian optical point spread function.

Open Script

Block Characteristics

Data Types	double
Direct Feedthrough	yes
Multidimensional Signals	no
Variable-Size Signals	no
Zero-Crossing Detection	no

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using Simulink® Coder™.

Not recommended for production-quality 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 widely-varying execution times. While the code is functionally valid and generally acceptable in resource-rich 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 Second-Order Integrator block because, for this block, the Model Discretizer produces an approximate discretization.