# genss

Generalized state-space model

## Description

Generalized state-space (`genss`) models are state-space models that include tunable parameters or components. `genss` models arise when you combine numeric LTI models with models containing tunable components (control design blocks). For more information about numeric LTI models and control design blocks, see Models with Tunable Coefficients.

You can use generalized state-space models to represent control systems having a mixture of fixed and tunable components. Use generalized state-space models for control design tasks such as parameter studies and parameter tuning with commands such as `systune` and `looptune`.

## Construction

To construct a `genss` model:

• Use `series`, `parallel`, `lft`, or `connect`, or the arithmetic operators `+`, `-`, `*`, `/`, `\`, and `^`, to combine numeric LTI models with control design blocks.

• Use `tf` or `ss` with one or more input arguments that is a generalized matrix (`genmat`) instead of a numeric array

• Convert any numeric LTI model, control design block, or `slTuner` interface (requires Simulink® Control Design™), for example, `sys`, to `genss` form using:

`gensys = genss(sys)`

When `sys` is an `slTuner` interface, `gensys` contains all the tunable blocks and analysis points specified in this interface. To compute a tunable model of a particular I/O transfer function, call `getIOTransfer(gensys,in,out)`. Here, `in` and `out` are the analysis points of interest. (Use `getPoints(sys)` to get the full list of analysis points.) Similarly, to compute a tunable model of a particular open-loop transfer function, use `getLoopTransfer(gensys,loc)`. Here, `loc` is the analysis point of interest.

## Properties

 `Blocks` Structure containing the control design blocks included in the generalized LTI model or generalized matrix. The field names of `Blocks` are the `Name` property of each control design block. You can change some attributes of these control design blocks using dot notation. For example, if the generalized LTI model or generalized matrix `M` contains a `realp` tunable parameter `a`, you can change the current value of `a` using:`M.Blocks.a.Value = -1;` `InternalDelay` Vector storing internal delays. 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 in the Control System Toolbox™ User's Guide. 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. However, the number of entries in `sys.InternalDelay` cannot change, because it is a structural property of the model. `InputDelay` Input delay for each input channel, specified as a scalar value or numeric vector. For continuous-time systems, specify input delays in the time unit stored in the `TimeUnit` property. For discrete-time systems, specify input delays in integer multiples of the sample time `Ts`. For example, ```InputDelay = 3``` means a delay of three sample times. For a system with `Nu` inputs, set `InputDelay` to an `Nu`-by-1 vector. Each entry of this vector is a numerical value that represents the input delay for the corresponding input channel. You can also set `InputDelay` to a scalar value to apply the same delay to all channels. Default: 0 `OutputDelay` Output delays. `OutputDelay` is a numeric vector specifying a time delay for each output channel. For continuous-time systems, specify output delays in the time unit stored in the `TimeUnit` property. For discrete-time systems, specify output delays in integer multiples of the sample time `Ts`. For example, ```OutputDelay = 3``` means a delay of three sampling periods. For a system with `Ny` outputs, set `OutputDelay` to an `Ny`-by-1 vector, where each entry is a numerical value representing the output delay for the corresponding output channel. You can also set `OutputDelay` to a scalar value to apply the same delay to all channels. Default: 0 for all output channels `Ts` Sample time. For continuous-time models, `Ts = 0`. For discrete-time models, `Ts` is a positive scalar representing the sampling period. This value is expressed in the unit specified by the `TimeUnit` property of the model. To denote a discrete-time model with unspecified sample time, set ```Ts = -1```. Changing this property does not discretize or resample the model. Use `c2d` and `d2c` to convert between continuous- and discrete-time representations. Use `d2d` to change the sample time of a discrete-time system. Default: `0` (continuous time) `TimeUnit` String representing the unit of the time variable. This property specifies the units for the time variable, the sample time `Ts`, and any time delays in the model. Use any of the following values: `'nanoseconds'``'microseconds'``'milliseconds'``'seconds'` `'minutes'``'hours'``'days'``'weeks'``'months'``'years'` Changing this property has no effect on other properties, and therefore changes the overall system behavior. Use `chgTimeUnit` to convert between time units without modifying system behavior. Default: `'seconds'` `InputName` Input channel names. Set `InputName` to a string for single-input model. For a multi-input model, set `InputName` to a cell array of strings. Alternatively, use automatic vector expansion to assign input names for multi-input models. For example, if `sys` is a two-input model, enter: `sys.InputName = 'controls';` The input names automatically expand to `{'controls(1)';'controls(2)'}`. You can use the shorthand notation `u` to refer to the `InputName` property. For example, `sys.u` is equivalent to `sys.InputName`. Input channel names have several uses, including: Identifying channels on model display and plotsExtracting subsystems of MIMO systemsSpecifying connection points when interconnecting models Default: Empty string `''` for all input channels `InputUnit` Input channel units. Use `InputUnit` to keep track of input signal units. For a single-input model, set `InputUnit` to a string. For a multi-input model, set `InputUnit` to a cell array of strings. `InputUnit` has no effect on system behavior. Default: Empty string `''` for all input channels `InputGroup` Input channel groups. The `InputGroup` property lets you assign the input channels of MIMO systems into groups and refer to each group by name. Specify input groups as a structure. In this structure, field names are the group names, and field values are the input channels belonging to each group. For example: ```sys.InputGroup.controls = [1 2]; sys.InputGroup.noise = [3 5];``` creates input groups named `controls` and `noise` that include input channels 1, 2 and 3, 5, respectively. You can then extract the subsystem from the `controls` inputs to all outputs using: `sys(:,'controls')` Default: Struct with no fields `OutputName` Output channel names. Set `OutputName` to a string for single-output model. For a multi-output model, set `OutputName` to a cell array of strings. Alternatively, use automatic vector expansion to assign output names for multi-output models. For example, if `sys` is a two-output model, enter: `sys.OutputName = 'measurements';` The output names automatically expand to `{'measurements(1)';'measurements(2)'}`. You can use the shorthand notation `y` to refer to the `OutputName` property. For example, `sys.y` is equivalent to `sys.OutputName`. Output channel names have several uses, including: Identifying channels on model display and plotsExtracting subsystems of MIMO systemsSpecifying connection points when interconnecting models Default: Empty string `''` for all output channels `OutputUnit` Output channel units. Use `OutputUnit` to keep track of output signal units. For a single-output model, set `OutputUnit` to a string. For a multi-output model, set `OutputUnit` to a cell array of strings. `OutputUnit` has no effect on system behavior. Default: Empty string `''` for all output channels `OutputGroup` Output channel groups. The `OutputGroup` property lets you assign the output channels of MIMO systems into groups and refer to each group by name. Specify output groups as a structure. In this structure, field names are the group names, and field values are the output channels belonging to each group. For example: ```sys.OutputGroup.temperature = [1]; sys.InputGroup.measurement = [3 5];``` creates output groups named `temperature` and `measurement` that include output channels 1, and 3, 5, respectively. You can then extract the subsystem from all inputs to the `measurement` outputs using: `sys('measurement',:)` Default: Struct with no fields `Name` System name. Set `Name` to a string to label the system. Default: `''` `Notes` Any text that you want to associate with the system. Set `Notes` to a string or a cell array of strings. Default: `{}` `UserData` Any type of data you want to associate with system. Set `UserData` to any MATLAB® data type. Default: `[]` `SamplingGrid` Sampling grid for model arrays, specified as a data structure. For model arrays that are derived by sampling one or more independent variables, this property tracks the variable values associated with each model in the array. This information appears when you display or plot the model array. Use this information to trace results back to the independent variables. Set the field names of the data 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 should be numeric and scalar valued, and all arrays of sampled values should match the dimensions of the model array. For example, suppose you create a 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, suppose you create a 6-by-9 model array, `M`, by independently sampling two variables, `zeta` and `w`. The following code attaches 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 example, the Simulink Control Design commands `linearize` and `slLinearizer` populate `SamplingGrid` in this way. Default: `[]`

## Examples

### Tunable Low-Pass Filter

This example shows how to create the low-pass filter F = a/(s + a) with one tunable parameter a.

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

1. Create a tunable real parameter.

```a = realp('a',10); ```

The `realp` object `a` is a tunable parameter with initial value 10.

2. Use `tf` to create the tunable filter `F`:

`F = tf(a,[1 a]);`

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

### 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.

Create a state-space model having the following state-space matrices:

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

where a and b are tunable parameters, whose initial values are –1 and 3, respectively.

1. Create the tunable parameters using `realp`.

``` a = realp('a',-1); b = realp('b',3);```
2. 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 `M = [1 2;0 -3]`, from the initial values of `a` and `b`.

3. Create the fixed-value state-space matrices.

```B = [-3.0;1.5]; C = [0.3 0]; D = 0;```
4. Use `ss` to create the state-space model.

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

`sys` is a generalized LTI model (`genss`) with tunable parameters `a` and `b`.

### Control System Model With Both Numeric and Tunable Components

This example shows how to create a tunable model of a control system that has both fixed plant and sensor dynamics and tunable control components.

Consider the the control system of the following illustration.

Suppose that the plant response is , and that the model of the sensor dynamics is . The controller is a tunable PID controller, and the prefilter is a low-pass filter with one tunable parameter, a.

Create models representing the plant and sensor dynamics. Because the plant and sensor dynamics are fixed, represent them using numeric LTI models.

```G = zpk([],[-1,-1],1); S = tf(5,[1 4]); ```

To model the tunable components, use Control Design Blocks. Create a tunable representation of the controller C.

```C = tunablePID('C','PID'); ```

`C` is a `tunablePID` object, which is a Control Design Block with a predefined proportional-integral-derivative (PID) structure.

Create a model of the filter with one tunable parameter.

```a = realp('a',10); F = tf(a,[1 a]); ```

`a` is a `realp` (real tunable parameter) object with initial value 10. Using `a` as a coefficient in `tf` creates the tunable `genss` model object `F`.

Interconnect the models to construct a model of the complete closed-loop response from r to y.

```T = feedback(G*C,S)*F ```
```T = Generalized continuous-time state-space model with 1 outputs, 1 inputs, 5 states, and the following blocks: C: Parametric PID controller, 1 occurrences. a: Scalar parameter, 2 occurrences. Type "ss(T)" to see the current value, "get(T)" to see all properties, and "T.Blocks" to interact with the blocks. ```

`T` is a `genss` model object. In contrast to an aggregate model formed by connecting only numeric LTI models, `T` keeps track of the tunable elements of the control system. The tunable elements are stored in the `Blocks` property of the `genss` model object. Examine the tunable elements of `T`.

```T.Blocks ```
```ans = C: [1x1 tunablePID] a: [1x1 realp] ```

When you create a `genss` model of a control system that has tunable components, you can use tuning commands such as `systune` to tune the free parameters to meet design requirements you specify.

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### Tips

• You can manipulate `genss` models as ordinary `ss` models. Analysis commands such as `bode` and `step` evaluate the model by replacing each tunable parameter with its current value.