hinfstructOptions

Options for hinfstruct

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Description

Use hinfstructOptions to define algorithm, display, and parallel options for the hinfstruct command.

Creation

Syntax

options = hinfstructOptions
options = hinfstructOptions(Name,Value)

Description

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

example

options = hinfstructOptions(Name,Value) specifies options by setting properties using one or more name-value pair arguments.

example

Properties

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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.

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.

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 set to "auto" or "on" to distribute independent optimization runs among MATLAB® workers (requires Parallel Computing Toolbox™ software).

Option to perform computation in parallel using a parallel pool of workers, specified as one of these values:

  • "off" — Run in serial on the MATLAB client. Enabling parallel computing may result in improved optimization performance. However, even with UseParallel set to "off", the algorithm can use built-in multithreading to make best use of the local resources. For more information, see MATLAB Multicore.

  • "auto" — Use a parallel pool if one is open or if MATLAB can automatically create one. If a parallel pool is not available, run in serial on the MATLAB client.

  • "on" — Use a parallel pool if one is open or if MATLAB can automatically create one. If a parallel pool is not available, throw an error.

When you use the RandomStart option to run multiple randomized optimization starts when tuning a structured controller, you can also use parallel computing to distribute the optimization runs among workers in a parallel pool.

If you do not have a parallel pool open and automatic pool creation is enabled, MATLAB opens a pool using the default cluster profile. To use a parallel pool to run computations in MATLAB, you must have Parallel Computing Toolbox. For more information, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).

Before R2026a: To run in parallel , set UseParallel to true (1).

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.

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.

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.

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

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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;

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="on");

Setting UseParallel to "auto" or "on" enables parallel processing by distributing the randomized starts among available workers in the parallel pool. For more information, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).

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

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See Also