clp = lft(mdiag(1,1/gam_opt,1)*Ghat, K_opt, 1, 1); eig(clp.a) % should be stable norm(clp, 'inf') % should be less than 1 Delta_hat = [0.2, 0.6; -0.2, -0.7] svd(Delta_hat) % should be less than or equal to 1 tmp = lft(Delta_hat, clp, 2, 2); eig(tmp.a) % closed loop is stable regardless of the structure of Delta_hat for k=1:100 Delta_hat = (rand(2,2)-0.5)*2; % 2-by-2 real random matrix whose elements are within from -1 to 1 v = max(svd(Delta_hat)); % the maximum singular value is set to v Delta_hat = Delta_hat / v; % normalization so that the resultant matrix has the maximu singular value as 1 tmp = lft(Delta_hat, clp, 2, 2); if max(real(eig(tmp.a))) > 0 break end end