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Gain scheduling is a control approach for controlling a nonlinear plant. To tune a gain-scheduled control system, you need a collection of linear models that approximate the nonlinear dynamics near selected design points. Generally, the dynamics of the plant are described by nonlinear differential equations of the form:

$$\begin{array}{l}\dot{x}=f\left(x,u,\sigma \right)\\ y=g\left(x,u,\sigma \right).\end{array}$$

Here, *x* is the state vector, *u* is the plant input,
and *y* is the plant output. These nonlinear differential equations can be
known explicitly for a particular system. More commonly, they are specified implicitly, such
as by a Simulink^{®} model.

You can convert these nonlinear dynamics into a family of linear models that describe the
local behavior of the plant around a family of operating points (*x*(*σ*),*u*(*σ*)), parameterized by the scheduling variables, *σ*. Deviations
from the nominal operating condition are defined as:

$$\delta x=x-x\left(\sigma \right),\text{\hspace{1em}}\delta u=u-u\left(\sigma \right).$$

These deviations are governed, to first order, by *linear
parameter-varying* dynamics:

$$\begin{array}{cc}\dot{\delta}x=A\left(\sigma \right)\delta x+B\left(\sigma \right)\delta u,& \delta y=C\left(\sigma \right)\delta x+D\left(\sigma \right)\delta u,\\ & \\ A\left(\sigma \right)=\frac{\partial f}{\partial x}\left(x\left(\sigma \right),u\left(\sigma \right)\right)& B\left(\sigma \right)=\frac{\partial f}{\partial u}\left(x\left(\sigma \right),u\left(\sigma \right)\right)\\ C\left(\sigma \right)=\frac{\partial g}{\partial x}\left(x\left(\sigma \right),u\left(\sigma \right)\right)& D\left(\sigma \right)=\frac{\partial g}{\partial u}\left(x\left(\sigma \right),u\left(\sigma \right)\right).\end{array}$$

This continuum of linear approximations to the nonlinear dynamics is called a linear parameter-varying (LPV) model:

$$\begin{array}{l}\frac{dx}{dt}=A\left(\sigma \right)x+B\left(\sigma \right)u\\ y=C\left(\sigma \right)x+D\left(\sigma \right)u.\end{array}$$

The LPV model describes how the linearized plant dynamics vary with time, operating
condition, or any other scheduling variable. For example, the pitch axis dynamics of an
aircraft can be approximated by an LPV model that depends on incidence angle,
*α*, air speed, *V*, and altitude, *h*.

In practice, you replace this continuum of plant models by a finite set of linear models
obtained for a suitable grid of *σ* values This replacement amounts to
sampling the LPV dynamics over the operating range and selecting a representative set of
*σ* values, your *design points*.

Gain-scheduled controllers yield best results when the plant dynamics vary smoothly between design points.

If you do not have this family of linear models, there are several approaches to obtaining it, including:

If you have a Simulink model, trim and linearize the model at the design points.

Linearize the Simulink model using parameter variation.

If the scheduling variable is time, linearize the model at a series of simulation snapshots.

If you have nonlinear differential equations that describe the plant, linearize them at the design points.

For tuning gain schedules, after you obtain the family of linear models, you must
associate it with an `slTuner`

interface to build a family of tunable
closed-loop models. To do so, use block substitution, as described in
Multiple Design Points in slTuner Interface.

This example shows how to linearize a plant model at a set of design points for tuning of a gain-scheduled controller. The example then uses the resulting linearized models to configure an `slTuner`

interface for tuning the gain schedule.

Open the `rct_CSTR`

model.

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

In this model, the `Concentration controller`

and `Temperature controller`

both depend on the output concentration `Cr`

. To set up this gain-scheduled system for tuning, you linearize the plant at a set of steady-state operating points that correspond to different values of the scheduling parameter `Cr`

. Sometimes, it is convenient to use a separate model of the plant for trimming and linearization under various operating conditions. For example, in this case, the most straightforward way to obtain these linearizations is to use a separate open-loop model of the plant, `rct_CSTR_OL`

.

```
mdl_OL = 'rct_CSTR_OL';
open_system(mdl_OL)
```

**Trim Plant at Design Points**

Suppose that you want to control this plant at a range of `Cr`

values from `4`

to `8`

. Trim the model to find steady-state operating points for a set of values in this range. These values are the design points for tuning.

Cr = (4:8)'; % concentrations for k=1:length(Cr) opspec = operspec(mdl_OL); % Set desired residual concentration opspec.Outputs(1).y = Cr(k); opspec.Outputs(1).Known = true; % Compute equilibrium condition [op(k),report(k)] = findop(mdl_OL,opspec,findopOptions('DisplayReport','off')); end

`op`

is an array of steady-state operating points. For more information about steady-state operating points, see About Operating Points.

**Linearize at Design Points**

Linearizing the plant model using `op`

returns an array of LTI models, each linearized at the corresponding design point.

```
G = linearize(mdl_OL,'rct_CSTR_OL/CSTR',op);
```

**Create slTuner Interface with Block Substitution**

To tune the control system `rct_CSTR`

, create an `slTuner`

interface that linearizes the system at those design points. Use block substitution to replace the plant in `rct_CSTR`

with the linearized plant-model array `G`

.

blocksub.Name = 'rct_CSTR/CSTR'; blocksub.Value = G; tunedblocks = {'Kp','Ki'}; ST0 = slTuner(mdl,tunedblocks,blocksub);

For this example, only the PI coefficients in the `Concentration controller`

are designated as tuned blocks. In general, however, `tunedblocks`

lists all the blocks to tune.

For more information about using block substitution to configure an `slTuner`

interface for gain-scheduled controller tuning, see Multiple Design Points in slTuner Interface.

For another example that illustrates using trimming and linearization to generate a family of linear models for gain-scheduled controller tuning, see Trimming and Linearization of the HL-20 Airframe.

If you are controlling the system around a reference trajectory
(*x*(*σ*),*u*(*σ*)),
use snapshot linearization to sample the system at various points along the
*σ* trajectory. Use this approach for time-varying systems where the
scheduling variable is time.

To linearize a system at a set of simulation snapshots, use a vector of positive scalars
as the `op`

input argument of `linearize`

,
`slLinearizer`

, or `slTuner`

. These scalars are the
simulation times at which to linearize the model. Use the same set of time values as the
design points in tunable surfaces for the system.

If the scheduling variable is a parameter in the Simulink model, you can use parameter variation to sample the control system over a
parameter grid. For example, suppose that you want to tune a model named
`suspension_gs`

that contains two parameters, `Ks`

and
`Bs`

. These parameters each can vary over some known range, and a
controller gain in the model varies as a function of both parameters.

To set up such a model for tuning, create a grid of parameter values. For this example,
let `Ks`

vary from 1 – 5, and let `Bs`

vary from 0.6 –
0.9.

Ks = 1:5; Bs = [0.6:0.1:0.9]; [Ksgrid,Bsgrid] = ndgrid(Ks,Bs);

These values are the design points at which to sample and tune the system. For example,
create an `slTuner`

interface to the model, assuming one tunable block, a
Lookup Table block named `K`

that models the
parameter-dependent gain.

params(1) = struct('Name','Ks','Value',Ksgrid); params(2) = struct('Name','Bs','Value',Bsgrid); STO = slTuner('suspension_gs','K',params);

`slTuner`

samples the model at all `(Ksgrid,Bsgrid)`

values specified in `params`

.

Next, use the same design points to create a tunable gain surface for parameterizing
`K`

.

design = struct('Ks',Ksgrid,'Bs',Bsgrid); shapefcn = @(Ks,Bs)[Ks,Bs,Ks*Bs]; K = tunableSurface('K',1,design,shapefcn); setBlockParam(ST0,'K',K);

After you parameterize all the scheduled gains, you can create your tuning goals and
tune the system with `systune`

.

Sometimes, your sampling grid includes points that represent irrelevant or unphysical
design points. You can eliminate such design points from the model grid entirely, so that
they do not contribute to any stage of tuning or analysis. To do so, use `voidModel`

,
which replaces specified models in a model array with `NaN`

.
`voidModel`

replaces specified models in a model array with
`NaN`

. Using `voidModel`

lets your design over a grid
of design points that is almost regular.

There are other tools for controlling which models contribute to design and analysis. For instance, you might want to:

Keep a model in the grid for analysis, but exclude it from tuning.

Keep a model in the grid for tuning, but exclude it from a particular design goal.

For more information, see Change Requirements with Operating Condition.

In MATLAB^{®}, you can use an array of LTI plant models to represent an LPV system sampled
at varying values of *σ*. To associate each linear model in the set with
the underlying design points, use the `SamplingGrid`

property of the LTI
model array *σ*. One way to obtain such an array is to create a parametric
generalized state-space (`genss`

) model of the system and sample the
model with parameter variation to generate the array. For an example, see Study Parameter Variation by Sampling Tunable Model.