approximate plant model by additive error methods -凯发k8网页登录

given a system g in lti form, the following commands reduce the system to any desired order you specify. the judgment call is based on its hankel singular values.

rng(1234,'twister');
g = rss(30,4,3); % random 30-state model
% balanced truncation to models with sizes 12:16
[g1,info1] = balancmr(g,12:16); 
% schur balanced truncation by specifying `maxerror'
[g2,info2] = schurmr(g,'maxerror',[1,0.8,0.5,0.2]);
sigma(g,'b-',g1,'r--',g2,'g-.')
legend('g','g1','g2')

the plot compares the original model g with the reduced models g1 and g2.

to determine whether the theoretical error bound is satisfied, calculate the peak difference across frequencies between the gain of the original system and the reduced system. compare that to the error bound stored in the info structure.

norm(g-g1(:,:,1),'inf') 

ans = 15.7214

info1.errorbound(1)

ans = 79.3580

or, plot the model error vs. error bound via the following commands:

[sv,w] = sigma(g-g1(:,:,1));
loglog(w,sv,w,info1.errorbound(1)*ones(size(w)))
xlabel('rad/sec');ylabel('sv');
title('error bound and model error')

see also

related topics

• approximate plant model by multiplicative error method