fluctuation after cancellation using ANFIS is 4, GA
is 1.8 and RLS is 1.2 times smaller as compared to
the fluctuation before cancellation.
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
-8
-6
-4
-2
0
2
4
6
8
x 10
-4
Tim e (s e c )
Defl ection (m)
Figure 5: Performance in implementing the AVC
algorithm using GA
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
-8
-6
-4
-2
0
2
4
6
8
x 10
-4
Tim e (s e c )
Defl ecti on (m)
Figure 6: Performance in implementing the AVC
algorithm using RLS
4 CONCLUDING REMARKS
This paper has presented the relative real-time
performance and error convergence issues in
implementing system identification and AVC system
of a flexible beam vibration using, ANFIS, GA and
RLS algorithm. A comparative performance of the
algorithms has been presented and discussed through
a set of experiments. For system identification, it is
noted that the execution time in implementing
ANFIS as compared to GA and RLS is significantly
higher. However, ANFIS shows slightly better error
convergence for the same number of iterations. On
the other hand, real-time computing performance of
GA varies based on the selection of the size of
population and binary representation. It is noted that
the GA with higher bit representation and larger
population size for the same error convergence
performs slower than ANFIS. It is also noted that the
execution time for each of the three algorithms is
less than the sampling time, in turn satisfying the
real-time requirement. However, in case of GA, this
is true only for population size 10 with 8 bit
representation.
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COMPARATIVE PERFORMANCE OF INTELLIGENT IDENTIFICATION AND CONTROL ALGORITHMS FOR A
FLEXIBLE BEAM VIBRATION
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