Before getting into the closed-loop system function of the CD/DVD case study, consider a few attributes of the open-loop system function by writing it out, leaving *K** _{a}* as the only variable not defined:

You can find the pole-zero plot by using PyLab and custom function splane(b,a) found at ssd.py. This function returns the system function numerator and denominator polynomial coefficients as ndarrays b and a. The plot is shown in the following figure.

In [<b>34</b>]: ssd.splane([1],[1,1275,31250,0],[-1400,100,-100,100])

Poles exist at *s* = –1,250, –25, and 0 rad/s, making the open-loop system function third-order. A causal LTI system is stable only if the poles are in the left-half plane, so you may be wondering, “How can this system produce a stable output with a pole at *s* = 0?” The answer: You need feedback.

Before considering the system with feedback, take a quick look at the open-loop impulse response,

Use PyLlab and the SciPy signal package function R,p,K = residue(b,a) to perform the partial fraction expansion. (** Note:** Residue is the continuous-time equivalent of residuez). Find the numerator and denominator polynomial coefficients by using the custom

*helper*function position_CD found at ssd.py.

For the case of *K** _{a}* = 50, here’s what the partial fraction expansion offers:

In [<b>65</b>]: b,a = ssd.position_CD(50,'open_loop') In [<b>66</b>]: R,p,K = signal.residue(b,a) In [<b>67</b>]: R # display partial fraction cefficients Out[<b>67</b>]: array([ 0.20787584, -10.3937922 , 10.18591636]) In [<b>68</b>]: p # display the corresponding system poles Out[<b>68</b>]: array([-1250., -25., 0.]) In [<b>69</b>]: K # no long division terms since proper rational Out[<b>69</b>]: array([ 0.])

Use table lookup to apply the inverse Laplace transform to each term to find the impulse response:

The first exponential term decays to zero much faster (about two orders of magnitude) than the second.

The poles also provide this information, because the system *time constant**s* are just one over the pole magnitudes, which are 1/25 = 0.8 ms and 1/1,250 = 40 ms for the first two terms.

The analysis here shows that you can approximate *G*_{0}(*s*) (a third-order system) with a second-order model. This helps, from a math complexity standpoint, for closed-loop analysis. The 0.8-ms time constant term (pole at 1,250 rad/s) in the open-loop model is insignificant, so it can be dropped next to the 40-ms time constant.

To make it okay to ignore the poles at 1,250 rad/s in *G*_{0}(*s*), factor the denominator to make sure the gain of this term is properly handled:

The last line is the reduced-order model for the open-loop system function.