hinfstructOptions

Set options for hinfstruct

Syntax

options = hinfstructOptions options = hinfstructOptions(Name,Value)

Description

options = hinfstructOptions returns the default option set for the hinfstruct command.

options = hinfstructOptions(Name,Value) creates an option set with the options specified by one or more Name,Value pair arguments.

Input Arguments

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

hinfstructOptions takes the following Name arguments:

`Display`	Determines the amount of information to display during `hinfstruct` optimization runs. `Display` takes the following values: `'off'` — `hinfstruct` runs in silent mode, displaying no information during or after the 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. `'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. Default: `'final'`
`MaxIter`	Maximum number of iterations in each optimization run. Default: 300
`RandomStart`	Number of additional optimizations starting from random values of the free parameters in the controller. 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). Default: 0
`UseParallel`	Parallel processing flag. 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. Default: `false`
`TargetGain`	Target H_∞ norm. 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. Default: 0
`TolGain`	Relative tolerance for termination. 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. Default: 0.001
`MaxFrequency`	Maximum closed-loop natural frequency. 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. Default: `Inf`
`MinDecay`	Minimum decay rate for closed-loop poles 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. Default: `1e-7`

Output Arguments

options

Option set containing the specified options for the hinfstruct command.

Examples

collapse all

Create Options Set for hinfstruct

Open Live Script

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