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
= hinfstructOptionshinfstruct
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 aProgress
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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