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Teljes Matlab script kiegészítő függvényekkel.
file: ccs2017_hf2_mo.m author: Peter Polcz <ppolcz@gmail.com>
Created on 2017. October 04. Reviewed on 2017. October 30.
┌d2017_10_26_hf2_mo │ - Persistence for `d2017_10_26_hf2_mo` reused (inherited) [run ID: 9200, 2017.10.30. Monday, 18:38:05] │ - Script `d2017_10_26_hf2_mo` backuped
A = [
-6 0 0
4 0 0
2 0 0
];
B = [ 1 ; 0 ; 0 ];
C = [ 0 0 1 ];
On = obsv(A,C)
Cn = ctrb(A,B)
rank(On)
rank(Cn)
orth(sym(Cn))
null(sym(On))
Hs = minreal(tf(ss(A,B,C,0)))
syms s t
clear H
H(s) = simplify(C/(s*eye(3) - A)*B)
h(t) = ilaplace(H)
disp 'NEM BIBO STABIL'
On = 0 0 1 2 0 0 -12 0 0 Cn = 1 -6 36 0 4 -24 0 2 -12 ans = 2 ans = 2 ans = [ 1, 0] [ 0, (2*5^(1/2))/5] [ 0, 5^(1/2)/5] ans = 0 1 0 Hs = 2 --------- s^2 + 6 s Continuous-time transfer function. H(s) = 2/(s*(s + 6)) h(t) = 1/3 - exp(-6*t)/3 NEM BIBO STABIL
a = 3;
b = 2;
c = 1;
d = 2;
n = a+b+c+d;
r = 1;
m = 1;
A_ = [
randn(a,a) zeros(a,b) randn(a,c) zeros(a,d)
randn(b,a) randn(b,b) randn(b,c) randn(b,d)
zeros(c,a) zeros(c,b) randn(c,c) zeros(c,d)
zeros(d,a) zeros(d,b) randn(d,c) randn(d,d)
];
B_ = [
randn(a,r)
randn(b,r)
zeros(c,r)
zeros(d,r)
];
C_ = [
randn(m,a) zeros(m,b) randn(m,c) zeros(m,d)
];
D = zeros(m,r);
System $(\bar A, \bar B, \bar C, D)$ is in Kalman decomposed form. The transfer function is reducible to a 3rd order system.
sys = ss(A_,B_,C_,D);
H = minreal(tf(sys))
H = 5.27 s^2 + 1.079 s + 9.61 --------------------------------- s^3 + 1.639 s^2 + 1.662 s + 3.532 Continuous-time transfer function.
Transform the system with a random transformation matrix $T$.
T = orth(rand(n));
A = T * A_ * T';
B = T * B_;
C = C_ * T';
sys = ss(A,B,C,D);
Controllability matrix
Cn = ctrb(A,B);
Transformation matrix, which generates the controllability staircase form.
[S1,~,~] = svd(Cn)
S1 = Columns 1 through 7 0.1851 -0.1260 0.6605 0.3331 -0.0259 0.0654 -0.4868 -0.4454 -0.3309 0.5066 0.1294 0.0792 -0.0298 0.6392 0.0858 0.5118 0.3553 -0.5421 0.5260 -0.0505 0.0978 -0.3697 0.2123 0.3263 -0.3468 -0.5290 0.1723 -0.2998 -0.7716 0.1020 -0.2560 0.0252 0.1330 -0.0619 -0.2651 0.0504 0.6169 0.0561 0.3454 -0.4374 -0.4296 0.2901 -0.1453 0.1048 0.0647 0.3751 0.4385 -0.4544 -0.2988 -0.0656 0.4070 -0.0381 0.4444 0.1876 0.7535 0.1060 Column 8 -0.4009 0.0543 -0.1472 0.4383 -0.4860 -0.1854 0.5788 0.1302
The transformation.
A1 = S1' * A * S1
B1 = S1' * B
C1 = C * S1
A1 = Columns 1 through 7 -1.8089 0.6452 0.1483 1.5986 0.8907 0.0490 0.1295 -0.1648 -0.3673 -2.1454 0.5573 0.3674 0.7582 -0.6061 0.1214 0.9705 0.5429 -1.3994 -0.7883 -0.6553 0.0855 -0.0008 -0.0089 0.0035 -0.1199 -1.3464 0.8948 0.0355 0.0020 0.0121 0.0126 0.2101 0.4802 -0.8108 -0.1284 0.0000 0.0000 0.0000 -0.0000 0.0000 -2.4875 0.2476 -0.0000 -0.0000 -0.0000 0.0000 -0.0000 0.1428 1.3063 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 0.1130 0.0064 Column 8 -0.7829 0.3596 -0.4607 0.0171 -0.3724 0.2728 -0.7555 0.5906 B1 = -0.2095 1.3140 0.1525 2.5173 0.2153 0.0000 0.0000 0.0000 C1 = Columns 1 through 7 -1.3935 0.0503 0.0561 1.7861 1.8917 0.0022 0.3711 Column 8 -0.3938
Order of the controllable subsystem
ab = rank(Cn);
2.1. Observability staircase form of the controllable subsystem
On1 = obsv(A1(1:ab,1:ab), C1(:,1:ab));
[~,~,S21] = svd(On1);
2.2. Observability staircase form of the uncontrollable subsystem
On2 = obsv(A1(ab+1:end,ab+1:end), C1(:,ab+1:end));
[~,~,S22] = svd(On2);
Second transformation matrix to generate the observability staircase form for both controllable and uncontrollable subsystems.
S2 = blkdiag(S21, S22)
S2 = Columns 1 through 7 0.5406 -0.2665 -0.3286 -0.5400 0.4870 0 0 -0.1069 0.3103 0.6597 -0.0538 0.6740 0 0 -0.1265 0.8059 -0.2901 -0.4789 -0.1454 0 0 -0.5703 -0.0446 -0.5775 0.2685 0.5168 0 0 -0.5958 -0.4258 0.1979 -0.6357 -0.1429 0 0 0 0 0 0 0 -0.0042 0.9914 0 0 0 0 0 -0.6858 -0.0978 0 0 0 0 0 0.7278 -0.0865 Column 8 0 0 0 0 0 -0.1305 -0.7212 -0.6804
Transformation
A2 = round(S2' * A1 * S2,10)
B2 = round(S2' * B1,10)
C2 = round(C1 * S2,10)
A2 = Columns 1 through 7 -1.7228 0.0510 0.0959 0 0 -0.2628 0 1.6409 0.2415 0.8973 0 0 0.1467 0 -0.3040 -2.1160 -0.1580 0 0 0.7482 0 1.3084 -0.3290 -0.6989 0.0519 -0.1450 0.6167 0.9176 -1.0943 -0.9964 -0.0245 1.5653 0.3143 0.2081 1.2303 0 0 0 0 0 1.3011 0 0 0 0 0 0 0.1434 -2.5055 0 0 0 0 0 0.7488 0.2298 Column 8 0 0 0 -0.7288 0.0807 0 0.0408 0.6139 B2 = -1.8370 0.3824 -0.5197 0.5085 2.0316 0 0 0 C2 = Columns 1 through 7 -2.9116 -0.4531 -0.1822 0 0 -0.5412 0 Column 8 0
Using minreal the transfer function which generates the Kalman decomposition can be retained easily, however, this does not work all the times. The uncontrollable subsystem is not always decomposed (since the uncontrollable subsystem is already redundant).
[H,U] = minreal(sys);
A1 = round(U*A/U,10)
B1 = round(U*B,10)
C1 = round(C/U,10)
5 states removed. A1 = Columns 1 through 7 -1.1425 -1.6553 0.2030 0 0 0.5666 -0.5119 -0.0107 0.0245 2.5022 0 0 -0.1765 0.1594 0.5187 -0.4917 -0.5213 0 0 0.0771 -0.0697 -0.2197 0.4070 1.4025 0.3210 -1.5640 -0.0442 0.2659 -1.3625 -0.2481 0.6331 0.1463 0.0452 0.8996 0.0772 0 0 0 0 0 0.6169 0 0 0 0 0 0 -0.7573 1.3011 0 0 0 0 0 -0.1920 -0.0812 Column 8 0 0 0 1.2256 -0.9628 0 0 -2.5085 B1 = 0.7434 1.1530 -1.3816 -2.0339 0.4989 0 0 0 C1 = Columns 1 through 7 1.2907 0.6553 -2.5731 0 0 -0.4015 0.3628 Column 8 0
End of the script.
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