# Talk:Beginning with CVX

### From Wikimization

(Difference between revisions)

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</pre> | </pre> | ||

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- | The paper its: Pole assignment of linear uncertain systems in a sector via a lyapunov - tipe approach. D. Arzeiler, J. Bernussou and G. Garcia. IEEE transactions automatic control, vol 38, nº 7, July 1993. | ||

Thanks a lot for all the ideas, they all are greats. | Thanks a lot for all the ideas, they all are greats. | ||

- | I don't know how to initialice Epsilon1 and Epsilon2 | + | I don't know how to initialice Epsilon1 and Epsilon2. At the article don't put anything. |

+ | |||

+ | I put off the while structure. | ||

Thanks a lot again. | Thanks a lot again. | ||

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Here is the new code: | Here is the new code: | ||

- | <pre> | + | <pre>%0)Initialization |

- | %0)Initialization | + | |

clear all; | clear all; | ||

n=2; m=1; | n=2; m=1; | ||

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end | end | ||

+ | %while para==0 | ||

%3)3 | %3)3 | ||

[v_W,D] = eig( full ( W ) ) | [v_W,D] = eig( full ( W ) ) | ||

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end | end | ||

- | lamda_W = min ( eig ( full ( W ) ) ) | ||

- | lamda_H = max ( eig ( full ( H ) ) ) | ||

R | R | ||

W=full(W) | W=full(W) | ||

K=R/W | K=R/W | ||

- | |||

</pre> | </pre> |

## Revision as of 10:02, 5 February 2009

lamda_W=eig(full(W))

Thanks a lot for all the ideas, they all are greats.

I don't know how to initialice Epsilon1 and Epsilon2. At the article don't put anything.

I put off the while structure.

Thanks a lot again.

Here is the new code:

%0)Initialization clear all; n=2; m=1; A_a=3*eye(2*n,2*n) B_a=4*eye(2*n,2*m) %1)1 W=eye(4) R=(zeros(2,4)) %2)2 H=W*A_a'+A_a*W-B_a*R-R'*B_a' lamda_W=min(eig(full(W))) lamda_H=max(eig(H)) Epsilon1=11; Epsilon2=0.1; if(lamda_W>=Epsilon1) if(lamda_H<=-Epsilon2) para=1 else para = 0 end else para =0 end %while para==0 %3)3 [v_W,D] = eig( full ( W ) ) [v_H,D] = eig( full ( H ) ) v_W_1 = v_W( : , 1 ) / norm ( v_W ( : , 1 ) ) ; v_W_2 = v_W( : , 2 ) / norm ( v_W ( : , 2 ) ) ; v_W_3 = v_W( : , 3 ) / norm ( v_W ( : , 3 ) ) ; v_W_4 = v_W( : , 4 ) / norm ( v_W ( : , 4 ) ) ; v_H_1 = v_H( : , 1 ) / norm ( v_H ( : , 1 ) ) ; v_H_2 = v_H( : , 2 ) / norm ( v_H ( : , 2 ) ) ; v_H_3 = v_H( : , 3 ) / norm ( v_H ( : , 3 ) ) ; v_H_4 = v_H( : , 4 ) / norm ( v_H ( : , 4 ) ) ; %4a)4a if ( Epsilon1 - lamda_W )>(lamda_H+Epsilon2) Caso=1 %For know where am I cvx_begin variables p1 p2 W(4,4) R(2,4) minimize (p1+p2) subject to W(1,1)<=p1 W(2,2)<=p1 W(1,1)>=Epsilon1 W(2,2)>=Epsilon1 W(3,3)==W(1,1) W(4,4)==W(2,2) R(1,1)>=-p2 R(1,1)<=p2 R(2,3)==R(1,1) R(1,2)>=-p2 R(1,2)<=p2 R(2,4)==R(1,2) H=W*A_a'+A_a*W-B_a*R-R'*B_a' v_W'*W*v_W - Epsilon1*eye(2*n) == semidefinite(2*n); cvx_end else %4b)4b Caso = 2 cvx_begin variables p1 p2 W(4,4) R(2,4) minimize (p1+p2) subject to W(1,1)>=Epsilon1 W(2,2)>=Epsilon1 W(1,1)<=p1 W(2,2)<=p1 W(3,3)==W(1,1) W(4,4)==W(2,2) R(1,1)>=-p2 R(1,1)<=p2 R(2,3)==R(1,1) R(1,2)>=-p2 R(1,2)<=p2 R(2,4)==R(1,2) H=W*A_a'+A_a*W-B_a*R-R'*B_a' Epsilon2*eye(2*n) + v_H'*H*v_H == -semidefinite(2*n); cvx_end end R W=full(W) K=R/W