hinfsynOptions
Option set for hinfsyn and
mixsyn
Description
Examples
Use the LMI-based algorithm to compute an -optimal controller for a plant with one control signal and two measurement signals. Turn on the display that shows the progress of the computation. Use hinfsynOptions to specify these algorithm options.
Load the plant and specify the numbers of measurements and controls.
load hinfsynExData P ncont = 1; nmeas = 2;
Create an options set for hinfsyn that specifies the LMI-based algorithm and turns on the display.
opts = hinfsynOptions('Method','LMI','Display','on');
Alternatively, start with the default options set, and use dot notation to change option values.
opts = hinfsynOptions; opts.Method = 'LMI'; opts.Display = 'on';
Compute the controller.
[K,CL,gamma] = hinfsyn(P,nmeas,ncont,opts);
Minimization of gamma:
Solver for linear objective minimization under LMI constraints
Iterations : Best objective value so far
1
2 223.728733
3 138.078240
4 138.078240
5 74.644885
6 48.270221
7 48.270221
8 48.270221
9 19.665676
10 19.665676
11 11.607238
12 11.607238
13 11.607238
14 4.067958
15 4.067958
16 4.067958
17 2.154349
18 2.154349
19 2.154349
20 1.579564
21 1.579564
22 1.579564
23 1.236726
24 1.236726
25 1.236726
26 0.993342
27 0.993342
28 0.949318
29 0.949318
30 0.949318
31 0.945762
32 0.944063
33 0.941246
34 0.941246
35 0.940604
*** new lower bound: 0.931668
Result: feasible solution of required accuracy
best objective value: 0.940604
guaranteed absolute accuracy: 8.94e-03
f-radius saturation: 0.404% of R = 1.00e+08
Optimal Hinf performance: 9.397e-01
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.
Example: 'Display','on','RelTol',0.05
General Options
Display optimization progress and generate report in the command window, specified
as the comma-separated pair consisting of 'Display' and
'on' or 'off'. The contents of the display
depend on the value of the 'Method' option.
For 'Method' = 'RIC', the display shows the range of
performance targets (gamma values) tested. For each
gamma, the display shows:
The smallest eigenvalues of the normalized Riccati solutions X = X∞/γ and Y = Y∞/γ
The spectral radius
rho(XY) = max(abs(eig(XY)))A pass/fail (
p/f) flag indicating whether thatgammavalue satisfies the conditions X ≥ 0, Y ≥ 0, andrho(XY)< 1The best achieved
gammaperformance value
For more information about the displayed information, see the Algorithms section
of hinfsyn.
For 'Method' = 'LMI', the display shows the best achieved
gamma value for each iteration of the optimization problem. It
also displays a report of the best achieved value and other parameters of the
computation.
Example: opts = hinfsynOptions('Display','on') creates an option
set that turns the progress display on.
Optimization algorithm that hinfsyn or
mixsyn uses to optimize closed-loop performance, specified as
the comma-separated pair consisting of 'Method' and one of the
following:
'RIC'— Riccati-based algorithm. The Riccati method is fastest, but cannot handle singular problems without first adding extra disturbances and errors. This process is called regularization, and is performed automatically byhinfsynandmixsynunless you set the'Regularize'option to'off'. With regularization, this method works well for most problems.When
'Method' = 'RIC', the additional options listed under Riccati Method Options are available.'LMI'— LMI-based algorithm. This method requires no regularization, but is computationally more intensive than the Riccati method.When
'Method' = 'LMI', the additional options listed under LMI Method Options are available.'MAXE'— Maximum-entropy algorithm.When
'Method' = 'MAXE', the additional options listed under Maximum-Entropy Method Options are available.
For more information about how these algorithms work, see the Algorithms section
of hinfsyn.
Example: opts = hinfsynOptions('Mathod','LMI') creates an option
set that specifies the LMI-based optimization algorithm.
Relative accuracy on the optimal H∞
performance, specified as the comma-separated pair consisting of
'RelTol' and a positive scalar value. The algorithm stops testing
γ values when the relative difference between the last failing
value and last passing value is less than RelTol.
Example: opts = hinfsynOptions('RelTol',0.05) creates an option
set that sets the relative accuracy to 0.05.
Riccati Method Options
Absolute accuracy on the optimal H∞
performance, specified as the comma-separated pair consisting of
'AbsTol' and a positive scalar value.
Example: opts = hinfsynOptions('AbsTol',1e-4) creates an option
set that sets the absolute accuracy to 0.0001.
Automatic plant scaling, specified as the comma-separated pair consisting of
'AutoScale' and one of the following:
'on'— Automatically scales the plant states, controls, and measurements to improve numerical accuracy.hinfsynalways returns the controllerKin the original unscaled coordinates.'off'— Does not change the plant scaling. Turning off scaling when you know your plant is well scaled can speed up the computation.
Example: opts = hinfsynOptions('AutoScale','off') creates an
option set that turns off automatic scaling.
Automatic regularization of the plant, specified as the comma-separated pair
consisting of 'Regularize' and one of:
'on'— Automatically regularizes the plant to enforce requirements on P12 and P21 (seehinfsyn). Regularization is a process of adding extra disturbances and errors to handle singular problems.'off'— Does not regularize the plant. Turning off regularization can speed up the computation when you know your problem is far enough from singular.
Example: opts = hinfsynOptions('Regularize','off') creates an
option set that turns off regularization.
Limit on controller gains, specified as the comma-separated pair consisting of
'LimitGain' and either 'on' or
'off'. For continuous-time plants, regularization of plant
feedthrough matrices D12 or
D21 (see hinfsyn) can result in controllers with large coefficients and fast
dynamics. Use this option to automatically seek a controller with the same performance
but lower gains and better conditioning.
LMI Method Options
Limit on norm of LMI solutions, specified as the comma-separated pair consisting
of 'LimitRS' and a scalar factor in the range [0,1]. Increase this
value to slow the controller dynamics by penalizing large-norm LMI solutions. See
[1].
Reduced-order synthesis tolerance, specified as the comma-separated pair
consisting of 'TolRS' and a positive scalar value.
hinfsyn computes a reduced-order controller when 1
<= rho(R*S) <= TolRs, where rho(A) is the
spectral radius, max(abs(eig(A))).
Maximum-Entropy Method Options
Frequency at which to evaluate entropy, specified as a real scalar value. For more
information, see the Algorithms section of hinfsyn.
Output Arguments
Options for the hinfsyn or mixsyn
computation, returned as an hinfsyn options object. Use the object
as an input argument to hinfsyn or mixsyn. For
example:
[K,CL,gamma,info] = hinfsyn(P,nmeas,ncont,opts);
References
[1] Gahinet, P., and P. Apkarian. "A linear matrix inequality approach to H∞-control." Int J. Robust and Nonlinear Control, Vol. 4, No. 4, 1994, pp. 421–448.
Version History
Introduced in R2018b
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