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Grid ureal parameters uniformly over their range


B = gridureal(A,N)
[B,SampleValues] = gridureal(A,N)
[B,SampleValues] = gridureal(A,NAMES,N)
[B,SampleValues] = gridureal(A,NAMES1,N1,NAMES2,N2,...)


B = gridureal(A,N) substitutes N uniformly-spaced samples of the uncertain real parameters in A. The samples are chosen to cut "diagonally" across the cube of real parameter uncertainty space. The array B has size equal to [size(A) N]. For example, suppose A has 3 uncertain real parameters, say X, Y and Z. Let (x1, x2 , , and xN) denote N uniform samples of X across its range. Similar for Y and Z. Then sample A at the points (x1, y1, z1), (x2, y2, z2), and (xN, yN, zN) to obtain the result B.

If A depends on additional uncertain objects, then B will be an uncertain object.

[B,SampleValues] = gridureal(A,N) additionally returns the specific sampled values (as a structure whose fieldnames are the names of A's uncertain elements) of the uncertain reals. Hence, B is the same as usubs(A,SampleValues).

[B,SampleValues] = gridureal(A,NAMES,N) samples only the uncertain reals listed in the NAMES variable (cell, or char array). Any entries of NAMES that are not elements of A are simply ignored. Note that gridureal(A, fieldnames(A.Uncertainty),N) is the same as gridureal(A,N).

[B,SampleValues] = gridureal(A,NAMES1,N1,NAMES2,N2,...) takes N1 samples of the uncertain real parameters listed in NAMES1, and N2 samples of the uncertain real parameters listed in NAMES2 and so on. size(B) will equal [size(A) N1 N2 ...].


collapse all

Create two uncertain real parameters gamma and tau. The nominal value of gamma is 4 and its range is 3 to 5. The nominal value of tau is 0.5 and its value can vary by +/- 30 percent.

gamma = ureal('gamma',4);
tau = ureal('tau',.5,'Percentage',30);

These uncertain parameters are used to construct an uncertain transfer function p. An integral controller, c, is synthesized for the plant p based on the nominal values of gamma and tau. The uncertain closed-loop system clp is formed.

p = tf(gamma,[tau 1]);
KI = 1/(2*tau.Nominal*gamma.Nominal);
c = tf(KI,[1 0]);
clp = feedback(p*c,1);

The figure below shows the open-loop unit step response (top plot) and closed-loop response (bottom plot) for a grid of 20 values of gamma and tau.

subplot(2,1,1); step(gridureal(p,20),6)
title('Open-loop plant step responses')
subplot(2,1,2); step(gridureal(clp,20),6)

The plot illustrates the low-frequency closed-loop insensitivity achieved by the PI control system.

This example illustrates the different options in gridding high-dimensional (e.g., n greater than 2) parameter spaces.

Construct an uncertain matrix, m, from four uncertain real parameters, a, b, c, and d, each making up the individual entries in m.

a = ureal('a',1);
b = ureal('b',2);
c = ureal('c',3);
d = ureal('d',4);
m = [a b;c d];

First, grid the (a,b) space at five places, and the (c,d) space at three places.

m1 = gridureal(m,{'a';'b'},5,{'c';'d'},3);

gridureal evaluates the uncertain matrix m at these 15 grid points, resulting in the numerical matrix m1.

Next, grid the (a,b,c,d) space at 15 places.

m2 = gridureal(m,{'a';'b';'c';'d'},15);

gridureal samples the uncertain matrix m at these 15 points, resulting in the numerical matrix m2.

The (2,1) entry of m is just the uncertain real parameter c. Plot the histograms of the (2,1) entry of both m1 and m2. The (2,1) entry of m1 only takes on three distinct values, while the (2,1) entry of m2 takes on 15 distinct values uniformly through its range.

title('2,1 entry of m1')
title('2,1 entry of m2')

See Also


Introduced before R2006a

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