The document describes a method to identify the notch frequency of a notch filter system using measurements of the input and output signals. It involves modeling the system as a second order notch filter and developing equations to directly calculate the notch frequency based on the input amplitude, output amplitude, and input frequency. Alternatively, a feedback control algorithm is proposed to iteratively converge on the notch frequency value by adjusting the input frequency and measuring the resulting output amplitudes at each step. Simulation results demonstrate the control algorithm can accurately estimate the notch frequency within a small number of iterations.

1. In this effort, we will assume that the dynamics of light incident on a spherical bead
satisfies the model of a notch filter with a given notch frequency n. The input into the system
consists of light of a given wavelength (correspondingly an excitation frequency ) and a known
magnitude denoted as A. The output from the system consists of a light waveform with a given
attenuated amplitude denoted as B.
System identification of a Notch Filter
The following methodology will utilize a measurement of B given a known input
waveform consisting of A and , to estimate the notch frequency n. To compute the notch
frequency we employ the model of a second order notch filter with a given transfer function
given by
22
22
2)(
)(
nn
n
ss
s
sR
sY




 . (1)
Figure 1 shows the resulting magnitude bode plot of (1).
10
2
10
3
10
4
10
5
10
6
-180
-160
-140
-120
-100
-80
-60
-40
-20
0
Magnitude(dB)
Bode Diagram
Frequency (rad/sec)
Figure 1: Magnitude bode plot of (1)
The input signal R(s) can be modeled as a pure cosine function described by tAtr cos)(  .
Using this excitation function into (1) produces a steady state solution for y(t) as follows
)sin()cos()( 21 tBtBty   , (2)
where 1B and 2B are defined as
)2(
)(4)(
),(
)(4)( 2222
22
2
22
2222
22
1 n
nn
n
n
nn
n A
B
A
B 










 . (3)

2. Using (2) and (3), we can compute the magnitude of the output signal B to be
2222
22
)(4)( nn
nA
B




 , which can be further simplified as follows
22
22





n
nA
B , (4)
From (4), if we choose an excitation frequency that is smaller than the notch frequency, then we
can ignore the absolute sign and solve for n to yield an explicit solution for n as follows

BA
BA
n


 . (5a)
If we choose an excitation frequency that is larger than the notch frequency, then we can replace
the absolute sign with a negative sign and solve for n to yield an explicit solution for n as
follows

BA
BA
n


 . (5b)
Now since we do not explicitly know what the notch frequency is, thus we can not
determine whether the excitation frequency is larger or smaller than n . However, we can
compute two estimates of the notch frequency for a given data set of A, B, . So, if we compute
another two estimates of the notch frequency for another data set that is relatively close to the
first data set, then we can compare which of the two estimates does not vary. Thus, if we know
the input and output signal amplitudes and the input signal frequency, we can determine the
system notch frequency from (5).
Determine Notch Frequency using Feedback Control
The following methodology will utilize a hybrid feedback control algorithm to
numerically converge to the system notch frequency. Specifically, we will employ the form of
the excitation signal r(t) given in the previous section such that we will use the excitation
frequency  as the control input.
At a given time step of the control algorithm, we will obtain two responses of the system
dynamics using the excitation function previously mentioned for two values of , i.e.,
  )1(),1( 21 kk oo , where )1( ko is the control frequency at a given step k -1
and  is a small, positive frequency. From these responses, we can compute the output
amplitudes denoted by B1 and B2 which we will use to update )(ko for the current time step.
We choose the following form for the control algorithm
  )1(
)1(
)1(
)1()1(sgn)( 1
12 


 k
kA
kB
kBkBKk oo  . (6)
where A(k-1) is the input amplitude of the (k-1)th time step, K is an arbitrary, positive, constant,
feedback control gain, and the sgn(·) is the sign function. The purpose of the sign function in (6)
is to determine which direction the frequency change should occur in order to converge to the
notch frequency. In addition, the term
)1(
)1(1


kA
kB
in (6) should decrease at every time step as we

3. approach the notch frequency, such that when it comes close enough to the notch frequency the
subsequent changes in (6) will only oscillate within two values of the excitation frequency. In
this region, the oscillation will occur as a result of the sign function since near the notch
frequency the sign function will repeatedly switch between positive and negative 1 while all
other terms in (6) will be invariant with respect to time step. Taking the average of oscillating
excitation frequencies should provide a good estimate for the notch frequency. Note that
choosing a small the control gain K results in higher accuracy estimates of the notch frequency.
However, the small values of K will also result in slower convergence rates to the notch
frequency.
The following is a step-by-step procedure for computing the notch frequency using this
methodology:
1) Guess an initial excitation frequency )1(o
2) Compute the amplitudes B1(1) and B2(1) using the system dynamics
3) Compute the new excitation frequency
)2(o using (6) when k=2
4) Repeat the following until
)1()(  kk oo 
is small:
a) Compute the amplitudes B1(k-1) and B2 (k-1) using the system dynamics
b) Compute the new excitation frequency )(ko using (6)
We simulated the control algorithm of (6) on the system dynamics of (1) using the
following parameters: 500)1( o , K = 5000, 000,10n . Figure 2 shows the resulting control
frequency time history. Within 8-9 time steps, figure 2 shows the control frequency has
converged to almost the exact value of the notch frequency.
0 2 4 6 8 10 12 14 16 18
5000
5500
6000
6500
7000
7500
8000
8500
9000
9500
10000
Time step
Frequencyrad/sec
Figure 2: Sample iteration control plot to compute the notch frequency