% This example illustrates the computation of approximation error % bounds (function "udistlfr") and the use of this information for % introducing new uncertain parameters modelling the approximations. % (See also ex_4_4.m). % Approximation is performed using the function "reduclfr" % Realization: lfrs a b c S1 = [3+0.001*a^5-b*c a^4*c;a*b*c^3+2 a^2-0.001*b*c^3+1]; % Approximation using "reduclfr" (this function handles the tolerance % argument of order reduction algorithms in such a way that approximation % error remains less than a given bound). S2 = reduclfr(S1,0.01,'a'); % Evaluation of the approximation error [distu,dist2,mindiff,maxdiff] = udistlfr(S1,S2); disp(mindiff); disp(maxdiff); % This function returns mindiff and maxdiff that are matrices having the % same size as S1. These matrices give term by term % - a lower bound of the minimum value of the approximation error (mindiff) % - an upper bound of the maximum value of the approximation error (maxdiff). % It remains to replace the approximation errors by additional normalized % real parameters. syserr = bnds2lfr('s_',mindiff,maxdiff); S3 = S2 + syserr; size(S3) % Now S3 has a Delta-block of size 14-by-14 to be compared to the original % size of S1 that is 23-by-23.