# Using FEEDBACK to Close Feedback Loops

This example shows why you should always use FEEDBACK to close feedback loops.

### Two Ways of Closing Feedback Loops

Consider the following feedback loop

where

K = 2;
G = tf([1 2],[1 .5 3])
G =

s + 2
---------------
s^2 + 0.5 s + 3

Continuous-time transfer function.

You can compute the closed-loop transfer function H from r to y in at least two ways:

• Using the feedback command

• Using the formula

$H=\frac{G}{1+GK}$

To compute H using feedback, type

H = feedback(G,K)
H =

s + 2
---------------
s^2 + 2.5 s + 7

Continuous-time transfer function.

To compute H from the formula, type

H2 = G/(1+G*K)
H2 =

s^3 + 2.5 s^2 + 4 s + 6
-----------------------------------
s^4 + 3 s^3 + 11.25 s^2 + 11 s + 21

Continuous-time transfer function.

### Why Using FEEDBACK is Better

A major issue with computing H from the formula is that it inflates the order of the closed-loop transfer function. In the example above, H2 has double the order of H. This is because the expression G/(1+G*K) is evaluated as a ratio of the two transfer functions G and 1+G*K. If

$G\left(s\right)=\frac{N\left(s\right)}{D\left(s\right)}$

then G/(1+G*K) is evaluated as:

$\frac{N}{D}{\left(\frac{D+KN}{D}\right)}^{-1}=\frac{ND}{D\left(D+KN\right)}.$

As a result, the poles of G are added to both the numerator and denominator of H. You can confirm this by looking at the ZPK representation:

zpk(H2)
ans =

(s+2) (s^2 + 0.5s + 3)
---------------------------------
(s^2 + 0.5s + 3) (s^2 + 2.5s + 7)

Continuous-time zero/pole/gain model.

This excess of poles and zeros can negatively impact the accuracy of your results when dealing with high-order transfer functions, as shown in the next example. This example involves a 17th-order transfer function G. As you did before, use both approaches to compute the closed-loop transfer function for K=1:

H1 = feedback(G,1);          % good
H2 = G/(1+G);                % bad

To have a point of reference, also compute an FRD model containing the frequency response of G and apply feedback to the frequency response data directly:

w = logspace(2,5.1,100);
H0 = feedback(frd(G,w),1);

Then compare the magnitudes of the closed-loop responses:

h = sigmaplot(H0,'b',H1,'g--',H2,'r');
legend('Reference H0','H1=feedback(G,1)','H2=G/(1+G)','location','southwest')
setoptions(h,'YlimMode','manual','Ylim',{[-60 0]})

The frequency response of H2 is inaccurate for frequencies below 2e4 rad/s. This inaccuracy can be traced to the additional (cancelling) dynamics introduced near z=1. Specifically, H2 has about twice as many poles and zeros near z=1 as H1. As a result, H2(z) has much poorer accuracy near z=1, which distorts the response at low frequencies. See the example Using the Right Model Representation for more details.