# LPV System

Simulate linear parameter-varying (LPV) systems

**Libraries:**

Control System Toolbox /
Linear Parameter Varying

## Description

A *linear parameter-varying* (LPV) system is a linear state-space
model whose dynamics vary as a function of certain time-varying parameters called
*scheduling parameters*. In MATLAB^{®}, an LPV model is represented in a state-space form using coefficients that are
parameter dependent.

Mathematically, you can represent an LPV system as follows.

$$\begin{array}{l}dx\left(t\right)=d{x}_{0}(p)+A\left(p\right)\left(x\left(t\right)-{x}_{0}\left(p\right)\right)+B\left(p\right)\left(u\left(t\right)-{u}_{0}(p)\right)\\ y\left(t\right)={y}_{0}\left(p\right)+C\left(p\right)\left(x\left(t\right)-{x}_{0}\left(p\right)\right)+D\left(p\right)\left(u\left(t\right)-{u}_{0}(p)\right)\\ x\left(0\right)={x}_{init}\end{array}$$

Here:

*u*(*t*) are the inputs*y*(*t*) are the outputs*x*(*t*) are the model states with initial value*x*_{init}$dx\left(t\right)$ is the state derivative vector $\dot{x}$ for continuous-time systems and the state update vector

*x*[*k*+1] for discrete-time systems. Here,*k*is the integer index that counts the number of sampling periods*T*._{s}*A*(*p*),*B*(*p*),*C*(*p*), and*D*(*p*) are the state-space matrices parameterized by the scheduling parameter vector*p*.The parameters

*p*=*p*(*t*) are measurable functions of the inputs and the states of the model. They can be a scalar quantity or a vector of several parameters. The set of scheduling parameters define the*scheduling space*over which the LPV model is defined.*dx*_{0}(*p*),*x*_{0}(*p*),*u*_{0}(*p*), and*y*_{0}(*p*) are the offsets in the values of $dx\left(t\right)$,*x*(*t*),*u*(*t*) and*y*(*t*) at a given parameter value*p*=*p*(*t*) or*p*[*k*].You can obtain the offsets by returning additional linearization information when calling functions such as

`linearize`

(Simulink Control Design) or`getIOTransfer`

(Simulink Control Design). For an example, see LPV Approximation of Boost Converter Model (Simulink Control Design).

**Caution**

Avoid making *C*(*p*) and
*D*(*p*) depend on the system output
**y**. Otherwise, the resulting state-space equation *y* =
*C*(*y*)*x* +
*D*(*y*)*u* creates an algebraic loop, because computing the output value
*y* requires knowing the output value. This algebraic loop is prone to
instability and divergence. Instead, try expressing *C* and
*D* in terms of the time *t*, the block input
**u**, and the state outputs **x**.

For similar reasons, avoid scheduling *A*(*p*) and
*B*(*p*) based on the **dx** output.
Note that it is safe for *A* and *B* to depend on
**y** when **y** is a fixed combination of states and
inputs (in other words, when *y* = *C**x* +
*D**u*, where *C* and *D* are constant
matrices).

The block implements a grid-based representation of the LPV system. You pick a grid of
values for the scheduling parameters. At each value *p* =
*p**, you specify the corresponding linear system as a state-space
(`ss`

or `idss`

(System Identification Toolbox)) model object. You use the generated array of state-space models to
configure the LPV System block.

The block accepts an array of state-space models with operating point information. The
block extracts the information on the scheduling variables from the
`SamplingGrid`

property of the LTI array. The scheduling variables define
the grid of the LPV models. They are scalar-valued quantities that can be functions of time,
inputs and states, or constants. They are used to pick the local dynamics in the operating
space. The software interpolates the values of these variables. The block uses this array with
data interpolation and extrapolation techniques for simulation.

## Examples

### Configure the Scheduling Parameter Input Port

Consider a 2-input, 3-output, 4-state LPV model. Use input
`u(2)`

and state `x(1)`

as scheduling parameters.
Configure the Simulink^{®} model as shown in the following figure.

### Simulate Linear Parameter-Varying System Using LPV System Block

Consider a linear mass-spring-damper system whose mass changes as a function of an external load command. The governing equation is as follows.

$$\mathit{m}\left(\mathit{u}\right)\ddot{\mathit{y}}+\mathit{c}\dot{\mathit{y}}+\mathit{ky}=\mathit{F}\left(\mathit{t}\right)$$

Here, $\mathit{m}\left(\mathit{u}\right)$ is the mass dependent upon the external command $\mathit{u}$, $\mathit{c}$ is the damping ratio, $\mathit{k}$ is the stiffness of the spring, and $\mathit{F}\left(\mathit{t}\right)$ is the forcing input. $\mathit{y}\left(\mathit{t}\right)$ is position of the mass at a given time $\mathit{t}$. For a fixed value of $\mathit{u}$, the system is linear and expressed as:

$$\mathit{A}=\left[\begin{array}{cc}0& 1\\ -\frac{\mathit{k}}{\mathit{m}}& -\frac{\mathit{c}}{\mathit{m}}\end{array}\right],$$

$$\mathit{B}=\left[\begin{array}{c}0\\ \frac{1}{\mathit{m}}\end{array}\right],$$

$$\mathit{C}=\left[\begin{array}{cc}1& 0\end{array}\right]$$

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

where, $\mathit{x}=\left[\begin{array}{c}\mathit{y}\\ \dot{\mathit{y}}\end{array}\right]$ is the state vector and $\mathit{m}$ is the value of the mass for a given value of $\mathit{u}$.

In this example, you want to study the model behavior over a range of input values from 1 to 10 Volts. For each value of $\mathit{u}$, measure the mass and compute the linear representation of the system. Suppose, mass is related to the input by the relationship $\mathit{m}\left(\mathit{u}\right)=10\mathit{u}+0.1{\mathit{u}}^{2}$. For values of `u`

ranging from 1 to 10 results in the following array of linear systems.

c = 5; k = 300; u = 1:10; m = 10*u + 0.1*u.^2; for i = 1:length(u) A = [0 1; -k/m(i), -c/m(i)]; B = [0; 1/m(i)]; C = [1 0]; sys(:,:,i) = ss(A,B,C,0); end

The variable $\mathit{u}$ is the scheduling input. Add this information to the model.

`sys.SamplingGrid = struct('LoadCommand',u);`

Configure the LPV System block:

Type

`sys`

in the**State-space array**field.Connect the input port

`par`

to a one-dimensional source signal that generates the values of the load command. If the source provides values between 1 and 10, the block uses interpolation to compute the linear model at a given time instance. Otherwise, the block uses extrapolation.

Simulate the LPV model to a constant forcing input of 100 N and random values for the load command scheduling variable.

```
model = "simMSDLPV";
open_system(model);
```

### Compare Nonlinear Model and LPV Block Response

This example shows how to simulate a linear parameter-varying (LPV) model of an engine speed using the LPV System block. The LPV System block interpolates a state-space array to model the LPV response. Typically, you can obtain such an array by batch linearizing a nonlinear model over a range of operating conditions. This example provides a linearized result of the engine speed model in `scdspeedlpvData`

. For more information about linearizing this model, see Linearize Engine Speed Model (Simulink Control Design).

Open the model.

```
model = "scdspeedLPVCompare";
open_system(model);
```

Load the linearization result for implementing the LPV model.

`load scdspeedlpvData.mat`

The LPV model is implemented under the LPV Model subsystem.

The LPV System block takes the throttle angle as an input and uses the speed output as scheduling variable. The block parameters are configured as shown in this image. Here, the state-space array `sys`

and offsets are obtained by batch linearizing the nonlinear model.

Simulate the model and plot the response comparison.

sim(model); plot(logsOut{1}.Values.Time,logsOut{1}.Values.Data) grid on legend("Nonlinear sim","LPV sim","LTI sim",Location="best")

The LPV model provides a good approximation of the nonlinear response.

## Extended Examples

## Limitations

Internal delays cannot be extrapolated to be less than their minimum value in the state-space model array.

When using an scattered grid of linear models to define the LPV system, only the nearest neighbor interpolation scheme is used. This may reduce the accuracy of simulation results. It is recommended to work with rectangular grids created using

`ndgrid`

.

## Ports

### Input

**u** — Input signal

scalar | vector

Specify the input signal *u*(*t*). In
multi-input case, this port accepts a signal of the dimension of the input.

**par** — Parameter values

scalar | vector

Provide the signals for variables defining the scheduling space (sampling grid
variables). The scheduling variables can be functions of time, inputs and states, or
constants. You can achieve the required dependence by preparing a scheduling signal
using clock input (for time), input signal (**u**), the output state
signals (**x**) of the LPV block, or any exogenous signals, as
required.

**Caution**

Avoid making *C*(*p*) and
*D*(*p*) depend on the system output
**y**. Otherwise, the resulting state-space equation *y* =
*C*(*y*)*x* +
*D*(*y*)*u* creates an algebraic loop, because computing the output value
*y* requires knowing the output value. This algebraic loop is
prone to instability and divergence. Instead, try expressing *C*
and *D* in terms of the time *t*, the block input
**u**, and the state outputs **x**.

For similar reasons, avoid scheduling *A*(*p*)
and *B*(*p*) based on the **dx**
output. Note that it is safe to for *A* and *B* to
depend on **y** when **y** is a fixed combination
of states and inputs, (in other words, when *y* = *C**x* +
*D**u* where *C* and *D* are constant
matrices).

### Output

**y** — Model output

scalar | vector

Response of the linear parameter-varying model.

**x** — State values

scalar | vector

Values of the model states

#### Dependencies

To enable this port, select **Output states** on the
**Outputs** tab of the block parameters.

**dx** — State derivative values

scalar | vector

Values of the state derivatives. The state derivatives are sometimes used to
define the scheduling parameters. However, avoid scheduling *A* and
*B* based on the **dx** output.

#### Dependencies

To enable this port, select **Output state derivatives (continuous-time)
or updates (discrete-time)** on the **Outputs** tab of
the block parameters.

**ss** — Local state-space model

bus

Local state-space model data at the major simulation time steps, returned as a bus signal with these elements.

`A`

— State matrix`B`

— Input matrix`C`

— Output matrix`D`

— Feedthrough matrix`InputDelay`

— Input delay`OutputDelay`

— Output delay`InternalDelay`

— Internal delays in the model

#### Dependencies

To enable this port, select **Output interpolated state-space
data** on the **Outputs** tab of the block
parameters.

**offset** — LPV model offsets

bus

LPV model offset data at the major simulation time steps, returned as a bus signal with these elements.

`InputOffset`

`OutputOffset`

`StateOffset`

`StateDerivativeOffset`

#### Dependencies

To enable this port, select **Output interpolated offsets** on
the **Outputs** tab of the block parameters.

## Parameters

To edit block parameters interactively, use the
Property Inspector. From the Simulink Toolstrip, on the **Simulation** tab, in the
**Prepare** gallery, select **Property
Inspector**.

### LPV Model Tab

**State-space array** — Array of state-space models

`ss`

or `idss`

model array

An array of state-space (`ss`

or `idss`

(System Identification Toolbox)) models. All the models in the
array must use the same definition of states.

The state-space array must specify scheduling parameters and the interpolation
grid for the model in the `SamplingGrid`

property. You can also
specify the model offsets in the `Offsets`

property of the array when
you set the **Operating Point Offsets** parameter to ```
Use
offsets in state-space array
```

, See the `ss`

model
reference page for more information on these properties.

When the block is in a model with synchronous state control (see the State Control (HDL Coder) block), you must specify an array of discrete-time models.

#### Programmatic Use

To set the block parameter value programmatically, use
the `set_param`

(Simulink) function.

Parameter: | `sys` |

Values: | `ss` or `idss` model array name in
quotes |

**Example: **`set_param(gcb,"sys","sysArrayName")`

**Initial state** — Initial conditions

0 (default) | vector

Initial conditions to use with the local model to start the simulation, specified as a vector of length equal to the number of model states.

#### Programmatic Use

To set the block parameter value programmatically, use
the `set_param`

(Simulink) function.

Parameter: | `x0` |

Values: | `"0"` (default) | initial state values in quotes |

**Example: **`set_param(gcb,"x0","[0 0.1]")`

**Operating Point Offsets** — Offset specification format

```
Specify Offsets as double
arrays
```

(default) | `Specify offsets as struct array`

| `Use offsets in state-space array`

*Since R2024a*

Specify the format for operating point offsets.

`Specify Offsets as double arrays`

— Specify offsets using the**Input offset**,**Output offset**,**State Offset**, and**State derivative/update offset**parameters.`Specify offsets as struct array`

— Specify offsets as a structure array with fields`u`

,`y`

,`x`

, and`dx`

specifying the input, output, state, and state derivative offsets, respectively.`Use offsets in state-space array`

— Use the offsets specified in the`Offsets`

property of the state-space array.

#### Programmatic Use

To set the block parameter value programmatically, use
the `set_param`

(Simulink) function.

Parameter: | `opSpecOption` |

Values: | ```
"Specify Offsets as double
arrays"
``` (default) | `"Specify offsets as struct array"` | `"Use offsets in state-space array"` |

**Example: **```
set_param(gcb,"opSpecOption","Specify offsets as struct
array")
```

**Input offset** — Input offset

`0`

(default) | array

Offsets in input `u(t)`

, specified as one of the
following:

`0`

— Use when there are no input offsets ($\overline{u}\left(p\right)=0\forall p$).Double vector of length equal to the number of inputs — Use when input offset is the same across the scheduling space.

Double array of size

*n*-by-1-by-_{u}*N*-by-_{1}`...`

-by-*N*array — Use when offsets are present and they vary across the scheduling space. Here,_{m}*n*is the number of inputs and_{u}*N*-by-_{1}`...`

-by-*N*is the size of the scheduling grid. For example, if your model has three inputs, two outputs, and four states and is scheduled over a 5-by-6 grid of operating points, the input array size must be 3-by-1-by-5-by-6. Use_{m}`size(sys)`

to determine the size of the state-space array`sys`

.

#### Dependencies

To enable this parameter, set the **Operating Point Offsets**
format to `Specify Offsets as double arrays`

.

#### Programmatic Use

To set the block parameter value programmatically, use
the `set_param`

(Simulink) function.

Parameter: | `uOffset` |

Values: | `"0"` (default) | array name in quotes |

**Example: **`set_param(gcb,"uOffset","uOffArray")`

**Output offset** — Output offset

`0`

(default) | array

Offsets in output `y(t)`

, specified as one of the
following:

`0`

— Use when there are no output offsets $\overline{y}\left(p\right)=0\forall p$.Double vector of length equal to the number of outputs. Use when output offsets are the same across the scheduling space.

Double array of size

*n*-by-1-by-_{y}*N*-by-_{1}`...`

-by-*N*array — Use when offsets are present and they vary across the scheduling space. Here,_{m}*n*is the number of outputs and_{y}*N*-by-_{1}`...`

-by-*N*is the size of the scheduling grid. For example, if your model has three inputs, two outputs, and four states and is scheduled over a 5-by-6 grid of operating points, the input array size must be 2-by-1-by-5-by-6. Use_{m}`size(sys)`

to determine the size of the state-space array`sys`

.

#### Dependencies

To enable this parameter, set the **Operating Point Offsets**
format to `Specify Offsets as double arrays`

.

#### Programmatic Use

To set the block parameter value programmatically, use
the `set_param`

(Simulink) function.

Parameter: | `yOffset` |

Values: | `"0"` (default) | array name in quotes |

**Example: **`set_param(gcb,"yOffset","yOffArray")`

**State offset** — State offset

`0`

(default) | array

Offsets in states `x(t)`

, specified as one of the
following:

`0`

— Use when there are no state offsets $\overline{x}\left(p\right)=0\forall p$.Double vector of length equal to the number of states. Use when the state offsets are the same across the scheduling space.

Double array of size

*n*-by-1-by-_{x}*N*-by-_{1}`...`

-by-*N*array — Use when offsets are present and they vary across the scheduling space. Here,_{m}*n*is the number of states and_{x}*N*-by-_{1}`...`

-by-*N*is the size of the scheduling grid. For example, if your model has three inputs, two outputs, and four states and is scheduled over a 5-by-6 grid of operating points, the input array size must be 4-by-1-by-5-by-6. Use_{m}`size(sys)`

to determine the size of the state-space array`sys`

.

#### Dependencies

To enable this parameter, set the **Operating Point Offsets**
format to `Specify Offsets as double arrays`

.

#### Programmatic Use

To set the block parameter value programmatically, use
the `set_param`

(Simulink) function.

Parameter: | `xOffset` |

Values: | `"0"` (default) | array name in quotes |

**Example: **`set_param(gcb,"xOffset","xOffArray")`

**State derivative/update offset** — State derivative or update offset

array

Offsets in state derivative or update variable `dx(t)`

, specified
as one of the following:

If you obtained the linear system array by linearization under equilibrium conditions, select the

**Assume equilibrium operating conditions**option. This option corresponds to an offset of $\overline{dx}\left(p\right)=0$ for a continuous-time system and $\overline{dx}\left(p\right)=\overline{x}\left(p\right)$ for a discrete-time system. This option is selected by default.If the linear system contains at least one system that you obtained under non-equilibrium conditions, clear the

**Assume equilibrium operating conditions**option. Specify one of the following in the**Offset value**field:If the

`dx`

offset values are the same across the scheduling space, specify as a double vector of length equal to the number of states.If the

`dx`

offsets are present and they vary across the scheduling space, specify as a double array of size*n*-by-1-by-_{x}*N*-by-_{1}`...`

-by-*N*array — Use when offsets are present and they vary across the scheduling space. Here,_{m}*n*is the number of states and_{x}*N*-by-_{1}`...`

-by-*N*is the size of the scheduling grid. For example, if your model has three inputs, two outputs, and four states and is scheduled over a 5-by-6 grid of operating points, the input array size must be 4-by-1-by-5-by-6. Use_{m}`size(sys)`

to determine the size of the state-space array`sys`

.

#### Dependencies

To enable this parameter, set the **Operating Point Offsets**
format to `Specify Offsets as double arrays`

and disable
**Assume equilibrium operating conditions**.

#### Programmatic Use

To set the block parameter value programmatically, use
the `set_param`

(Simulink) function.

Parameter: | `dxOffset` |

Values: | `"0"` (default) | array name in quotes |

**Example: **`set_param(gcb,"dxOffset","dxOffArray")`

**Struct array** — Offsets structure array

`struct`

(default) | structure array name

*Since R2024a*

Model offsets, specified as a structure with these fields.

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

`u` | Input offsets |

`y` | Output offsets |

`x` | State offsets |

`dx` | State derivative offsets |

If offset values are the same across the scheduling space, specify as a double vector of length equal to the number of inputs, outputs, or states for the corresponding fields.

If the offsets vary across scheduling space, specify a structure array. For instance, suppose that your model has three inputs, two outputs, and four states. If you linearize your model using a 5-by-6 array of operating points, the structure array size must be 5-by-6 and each entry must contain a vector of length equal to the number of inputs, outputs, or states for the corresponding fields.

The `linearize`

(Simulink Control Design) function returns offsets in
this format in the `info.Offsets`

output when you linearize with
`StoreOffsets`

option set to `true`

.

#### Dependencies

To enable this parameter, set the
**Operating Point Offsets** format to ```
Specify
Offsets as struct array
```

.* (since R2024a)*

#### Programmatic Use

To set the block parameter value programmatically, use
the `set_param`

(Simulink) function.

Parameter: | `Offset` |

Values: | `"struct"` (default) | structure array name in quotes |

**Example: **`set_param(gcb,"Offset","OffsetStructName")`

### Fixed Entries Tab

**Nominal model** — Nominal LPV model

```
Use the first model in state-space
array
```

(default) | `Custom value`

State-space model that provides the values of the fixed coefficients, specified as one of the following:

`Use the first model in state-space array`

(**Default:**) — The first model in the state-space array is used to represent the LPV model. In the following example, the state-space array is specified by object`sys`

and the fixed coefficients are taken from model`sys(:,:,1)`

.% Specify a 4-by-5 array of state-space models. sys = rss(4,2,3,4,5); a = 1:4; b = 10:10:50; [av,bv] = ndgrid(a,b); % Use "alpha" and "beta" variables as scheduling parameters. sys.SamplingGrid = struct('alpha',av,'beta',bv);

Fixed coefficients are taken from the model

`sysFixed = sys(:,:,1)`

, which corresponds to`[alpha=1, beta=10]`

. If the (2,1) entry of`A`

matrix is forced to be fixed, its value used during the simulation is`sysFixed.A(2,1)`

.`Custom value`

— Specify a different state-space model for fixed entries. Specify a variable for the fixed model in the**State space model**field. The fixed model must use the same state basis as the state-space array in the LPV model.

**Fixed Coefficient Indices** — Fixed coefficients of state-space matrices

`false`

or `0`

(default) | `true`

or `1`

| logical matrix

Specify which coefficients of the state-space matrices and delay vectors are fixed.

Specify one of the following:

Scalar Boolean (

`true`

or`false`

), if all entries of a matrix are to be treated the same way.The default value is

`false`

for the state-space matrices and delay vectors, which means that they are treated as free.Logical matrix of a size compatible with the size of the corresponding matrix:

State-space matrix

Size of fixed entry matrix

**A matrix***n*-by-_{x}*n*_{x}**B matrix***n*-by-_{x}*n*_{u}**C matrix***n*-by-_{y}*n*_{x}**D matrix***n*-by-_{y}*n*_{u}**Input delay***n*-by-1_{u}**Output delay***n*-by-1_{y}**Internal delay***n*-by-1_{i}where,

*n*is the number of inputs,_{u}*n*is the number of outputs,_{y}*n*is the number of states,_{x}*n*is the length of internal delay vector._{i}Numerical indices to specify the location of fixed entries. See

`sub2ind`

reference page for more information on how to generate numerical indices corresponding to a given subscript`(i,j)`

for an element of a matrix.

### Scheduling Tab

**Interpolation method** — Interpolation method

`Linear`

(default) | `Nearest`

| `Flat`

Interpolation method. Defines how the state-space data must be computed for scheduling parameter values that are located away from their grid locations.

Specify one of the following options:

`Flat`

— Choose the state-space data at the grid point closest, but not larger than, the current point. The*current point*is the value of the scheduling parameters at current time.`Nearest`

— Choose the state-space data at the closest grid point in the scheduling space.`Linear`

— Obtain state-space data by linear interpolation of the nearest 2d neighbors in the scheduling space, where d = number of scheduling parameters.

The default interpolation scheme is `Linear`

for regular
grids of scheduling parameter values. For irregular grids, the
`Nearest`

interpolation scheme is always used regardless of
the choice you made. To learn more about regular and irregular grids, see Regular vs. Irregular Grids.

The `Linear`

method provides the highest accuracy but
takes longer to compute. The `Flat`

and
`Nearest`

methods are good for models that have
mode-switching dynamics.

#### Programmatic Use

To set the block parameter value programmatically, use
the `set_param`

(Simulink) function.

Parameter: | `IMethod` |

Values: | `"Linear"` (default) | `"Nearest"` | `"Flat"` |

**Example: **`set_param(gcb,"IMethod","Flat")`

**Extrapolation method** — Extrapolation method

`Clip`

(default) | `Linear`

Extrapolation method. Defines how to compute the state-space data for scheduling
parameter values that fall outside the range over which the state-space array has been
provided (as specified in the `SamplingGrid`

property).

Specify one of the following options:

`Clip`

(default) — Disables extrapolation and returns the data corresponding to the last available scheduling grid point that is closest to the current point.`Linear`

— Fits a line between the first or last pair of values for each scheduling parameter, depending upon whether the current value is less than the first or greater than the last grid point value, respectively. This method returns the point on that line corresponding to the current value. Linear extrapolation requires that the interpolation scheme be linear too.

#### Programmatic Use

To set the block parameter value programmatically, use
the `set_param`

(Simulink) function.

Parameter: | `EMethod` |

Values: | `"Clip"` (default) | `"Linear"` |

**Example: **`set_param(gcb,"EMethod","Linear")`

**Index search method** — Prelookup algorithm

`Binary search`

(default) | `Linear search`

The block determines the location of the current scheduling parameter values in
the scheduling space using a prelookup algorithm. Select ```
Linear
search
```

or `Binary search`

. Each search method
has speed advantages in different situations. For more information on this parameter,
see the Prelookup (Simulink) block reference page.

#### Programmatic Use

To set the block parameter value programmatically, use
the `set_param`

(Simulink) function.

To get the block parameter value
programmatically, use the `get_param`

(Simulink) function.

Parameter: | `IndexSearch` |

Values: | `"Binary Search"` (default) | `"Linear Search"` |

**Example: **```
set_param(gcb,"IndexSearch","Linear
Search")
```

**Begin index search using previous index result** — Begin search at previous time step

`on`

(default) | `off`

Select this check box when you want the block to start its search using the index found at the previous time step. For more information on this parameter, see the Prelookup (Simulink) block reference page.

#### Programmatic Use

To set the block parameter value programmatically, use
the `set_param`

(Simulink) function.

Parameter: | `IndexBegin` |

Values: | `"on"` (default) | `"off"` |

**Example: **`set_param(gcb,"IndexBegin","off")`

### Code Generation Tab

**Block data type (discrete-time case only)** — Block data type

`double`

(default) | `single`

Block data type, specified as `double`

or
`single`

.

#### Dependencies

To enable this option, use a discrete-time state-space model as input.

#### Programmatic Use

To set the block parameter value programmatically, use
the `set_param`

(Simulink) function.

Parameter: | `DataType` |

Values: | `"double"` (default) | `"single"` |

**Example: **`set_param(gcb,"DataType","single")`

**Initial buffer size for delays** — Initial buffer size

`1024`

(default) | positive integer greater than 5

Initial memory allocation for the number of input points to store for models that contain delays. If the number of input points exceeds the initial buffer size, the block allocates additional memory. The default size is 1024.

When you run the model in Accelerator mode or build the model, make sure the initial buffer size is large enough to handle maximum anticipated delay in the model.

#### Programmatic Use

To set the block parameter value programmatically, use
the `set_param`

(Simulink) function.

Parameter: | `InitBufferSize` |

Values: | `"1024"` (default) | positive integer greater than 5 in quotes |

**Example: **`set_param(gcb,"InitBufferSize","512")`

**Use fixed buffer size** — Use fixed buffer size

`off`

(default) | `on`

Specify whether to use a fixed buffer size to save delayed input and output data from previous time steps. Use this option for continuous-time LPV systems that contain input or output delays. If the buffer is full, new data replaces data already in the buffer. The software uses linear extrapolation to estimate output values that are not in the buffer.

#### Programmatic Use

To set the block parameter value programmatically, use
the `set_param`

(Simulink) function.

Parameter: | `FixedBuffer` |

Values: | `"off"` (default) | `"on"` |

**Example: **`set_param(gcb,"FixedBuffer","on")`

## Extended Capabilities

### C/C++ Code Generation

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

## Version History

**Introduced in R2014b**

### R2024a: Improved support for offsets and other enhancements

The LPV System block now supports specifying operating point offsets in two new formats:

Specify offsets as struct array

Use offsets in state-space array

Before R2024a, the block supported specifying offsets only as double arrays in the block
parameters. Use the **Operating Point Offsets** list to specify the
format of offsets.

Additionally, the block has the following new enhancements:

The block architecture now uses a Varying State Space block underneath. This streamlines the support for offsets.

The block now supports fast restart for simulation. In this mode, only the block inputs and the

**Initial state**parameter are tunable. The rest of the block parameters are non tunable because changing them alters the model topology.

### R2024a: Ignores constant parameters in `SamplingGrid`

The LPV System block now ignores singleton dimension and constant
parameters in the sampling grid of the input model array. Therefore, you must not feed the
parameters, that is, the fields of the `SamplingGrid`

structure with
constant values to the **par** port of the block. Use only the varying
fields with this port.

Before R2024a, the block returned an error when fed parameters with constant values.

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