# hinfstructOptions

Options for `hinfstruct`

## Description

Use `hinfstructOptions`

to define algorithm, display, and parallel
options for the `hinfstruct`

command.

## Creation

### Description

returns the
default option set for the `options`

= hinfstructOptions`hinfstruct`

command.

specifies options by setting properties using one or more name-value pair arguments.`options`

= hinfstructOptions(`Name,Value`

)

## Properties

`Display`

— Amount of information to display

`'final'`

(default) | `'iter'`

| `'off'`

Amount of information to display during `hinfstruct`

optimization
runs, specified as one of the following values:

`'final'`

— Display a one-line summary at the end of each optimization run. The display includes the minimized value of the closed-loop*H*_{∞}norm and the number of iterations for each run.`'iter'`

— Display optimization progress after each iteration. The display includes the value of the closed-loop*H*_{∞}norm after each iteration. The display also includes a`Progress`

value indicating the percent change in the*H*_{∞}norm from the previous iteration.`'off'`

—`hinfstruct`

runs in silent mode, displaying no information during or after the run.

`MaxIter`

— Maximum number of iterations

300 (default) | positive integer

Maximum number of iterations in each optimization run, specified as a positive integer. The optimization run terminates after this number of iterations, regardless of whether the run has converged.

`RandomStart`

— Number of additional optimizations

0 (default) | nonnegative integer

Number of additional optimizations starting from random values of the free parameters in the controller, specified as a nonnegative integer.

If `RandomStart = 0`

, `hinfstruct`

performs a single optimization run starting from the initial values of the tunable
parameters. Setting `RandomStart = N > 0`

runs
*N* additional optimizations starting from *N*
randomly generated parameter values.

`hinfstruct`

finds a local minimum of the gain minimization
problem. To increase the likelihood of finding parameter values that meet your design
requirements, set `RandomStart > 0`

. You can then use the
best design that results from the multiple optimization runs.

Use with `UseParallel = true`

to distribute independent
optimization runs among MATLAB^{®} workers (requires Parallel Computing Toolbox™ software).

`UseParallel`

— Parallel processing flag

`false`

(default) | `true`

Parallel processing flag, specified as `false`

or
`true`

. When you use the `RandomStart`

option to run
multiple randomized optimization starts, you can also use parallel computing to speed up
tuning by distributing the optimization runs among workers in a parallel pool. Set this
flag to `true`

to enable parallel processing. If there is an available
parallel pool, then the software performs independent optimization runs concurrently
among workers in that pool. If no parallel pool is available, one of the following
occurs:

If

**Automatically create a parallel pool**is selected in your Parallel Computing Toolbox preferences (Parallel Computing Toolbox), then the software starts a parallel pool using the settings in those preferences.If

**Automatically create a parallel pool**is not selected in your preferences, then the software performs the optimization runs successively, without parallel processing.

If **Automatically create a parallel pool** is not selected in your
preferences, you can manually start a parallel pool using `parpool`

(Parallel Computing Toolbox) before running the tuning command.

Using parallel processing requires Parallel Computing Toolbox software.

`TargetGain`

— Target *H*_{∞} norm

0 (default) | scalar | `Inf`

Target *H*_{∞} norm, specified as a
scalar value or `Inf`

. The `hinfstruct`

optimization
stops when the *H*_{∞} norm (peak
closed-loop gain) falls below the specified `TargetGain`

value.

Set `TargetGain = 0`

to optimize controller performance
by minimizing the peak closed-loop gain. Set
`TargetGain = Inf`

to just stabilize the closed-loop
system.

`TolGain`

— Relative tolerance for termination

0.001 (default) | positive scalar

Relative tolerance for termination specified as a positive scalar value. The
optimization terminates when the *H*_{∞}
norm decreases by less than `TolGain`

over 10 consecutive iterations.
Increasing `TolGain`

speeds up termination, and decreasing
`TolGain`

yields tighter final values.

`MaxFrequency`

— Maximum closed-loop natural frequency

`Inf`

(default) | positive scalar

Maximum closed-loop natural frequency, specified as a positive scalar value. Setting
`MaxFrequency`

constrains the closed-loop poles to satisfy
`|p| < MaxFrequency`

.

To let `hinfstruct`

choose the closed-loop poles automatically
based upon the system's open-loop dynamics, set
`MaxFrequency = Inf`

. To prevent unwanted fast dynamics or
high-gain control, set `MaxFrequency`

to a finite value.

Specify `MaxFrequency`

in units of 1/`TimeUnit`

,
relative to the `TimeUnit`

property of the system you are tuning.

`MinDecay`

— Minimum decay rate for closed-loop poles, specified as a positive scalar value

`1e-7`

(default) | positive scalar

Minimum decay rate for closed-loop poles, specified as a positive scalar value.
Constrains the closed-loop poles to satisfy
`Re(p) < -MinDecay`

. Increase this value to improve the
stability of closed-loop poles that do not affect the closed-loop gain due to pole/zero
cancellations.

Specify `MinDecay`

in units of 1/`TimeUnit`

,
relative to the `TimeUnit`

property of the system you are tuning.

## Examples

### Create Options Set for hinfstruct

Create an options set for a `hinfstruct`

run using three random restarts and a stability offset of 0.001. Also, configure the `hinfstruct`

run to stop as soon as the closed-loop gain is smaller than 1.

options = hinfstructOptions('TargetGain',1,... 'RandomStart',3,'StableOffset',1e-3);

Alternatively, use dot notation to set the values of `options`

.

options = hinfstructOptions; options.TargetGain = 1; options.RandomStart = 3; options.StableOffset = 1e-3;

### Configure Option Set for Parallel Optimization Runs

When you use the `RandomStart`

option to run
multiple randomized optimization starts, you can also use parallel computing to speed up
tuning by distributing the optimization runs among workers. (Using parallel computing
requires a Parallel Computing Toolbox license.) For this example, configure an option set for
`hinfstruct`

with 20 independent optimization restarts, executed
concurrently on multiple workers in a parallel pool.

If **Automatically create a parallel pool** is not selected in your
Parallel Computing Toolbox preferences (Parallel Computing Toolbox), manually start a parallel pool using `parpool`

(Parallel Computing Toolbox).

parpool;

If **Automatically create a parallel pool** is selected in your
preferences, you do not need to manually start a pool.

Create an `hinfstructOptions`

set that specifies 20 random restarts
to run in parallel.

options = hinfstructOptions('RandomStart',20,'UseParallel',true);

Setting `UseParallel`

to `true`

enables parallel
processing by distributing the randomized starts among available workers in the parallel
pool.

Use the `hinfstructOptions`

set when you call
`hinfstruct`

. For example, suppose you have already created a tunable
closed loop model `CL0`

. In this case, the following command uses
parallel computing to tune `CL0`

.

[CL,gamma,info] = hinfstruct(CL0,options);

## Version History

**Introduced in R2010b**

## See Also

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