Nonlinear Structural Control Using Magnetorheological Damper - Design Optimization of Active and Passive Structural Control Systems - page 312

Figure 7. PSO convergence for all cost functions showing the decay in total cost function value and the
respective vales in the each associated costs

Hybrid Semi Active Control
of Base Isolated Building

µ max and µ min are determined through experiments
tests.
The Wen's hysteretic variable ( z w ) is given by
(Sahasrabuddhe and Nagarajaiah, 2005),

Experimental and numerical studies of the building
with GA-FLC monitored MR damper for eight
earthquake records are carried out. For brevity,
results are reported only for N. Palm Springs
earthquake excitation. The time history and the
frequency content of the excitation are shown
in Figure 8. It should be noted that most of the
energy of the excitation is localized below 5Hz
of frequency. This shows that the first mode of
the base isolated building will be highly effected
by the N. Palm Springs earthquake excitation. In
the present section we discuss the experimental
results obtained on the three storey base isolated
building and compare them with that of results
obtained from the numerical simulations.
The responses measured in the experimental
study are all the floor inter-storey drifts, relative
base displacement, absolute displacement at shake
table and acceleration at all the floors, base and
shake table. Experimental data are acquired using
Dewetron acquisition system at a sampling rate
of 1kHz. The acquisition system has a low pass
filter of 30 Hz. No further data filtering is carried
out offline. Control actions are computed using

  

2



Y z + γ | x | z + β x z − A x = 0

w w b w w b w w b

(13)

where the constants Y , A w , γ w , and β w are the
shape parameters of the hysteretic loop which are
calibrated using the experimental data. Tests with
sinusoid input excitations at the base are carried
out with varied frequencies ranging from 1Hz to
3Hz and amplitudes ranging from 2mm to 10mm,
to determine frictional damping at the sliding
isolator. Particle swarm optimization algorithm
is used to optimize the variables µ min , µ max , Y
and λ . The values for A w , γ w , and β w are con-
sidered as 1, 0.9 and 0.1, respectively (Sahasrabud-
dhe and Nagarajaiah, 2005; Madden et al., 2002).
The optimal values for the variables are obtained
as µ min = 2853 , µ max = 1 1303 , Y = 0 2526 . .

and λ = 0 6191

.

.