Geology Reference
In-Depth Information
The main disadvantage of the clipped optimal
strategy is that it tries to change the voltage of the
MR damper directly from 0 to its maximum value
(in the present case 5 V), without any intermedi-
ate voltage supply. This makes the controller a
sub-optimal one. This swift change in voltage
leads to a sudden rise in the external control
force, which increases the system responses (Ali
and Ramaswamy, 2008a, 2009b). Moreover, the
clipped optimal strategy needs the measurement
of the force the damper provides. The mathemati-
cal information regarding the structure is used
for the calculation of the numerically obtained
control forces to compare with the experimentally
obtained damper force. Based on the compared
result an on-off strategy is used to keep the
damper input voltage to zero or to change it to
maximum, and vice versa. Therefore, there is a
need for control algorithms which can change
the MR damper voltage, slowly and smoothly,
such that all voltage values between maximum
and zero voltage can be covered based on the
feedback from the structure.
In this context various intelligent methods
(neural controllers (Xu et al., 2003) and non-
adaptive and adaptive fuzzy controllers by Ali and
Ramaswamy (2008a)) have been tried in which the
damper monitoring voltage is directly set based
on system feedback. Ali and Ramaswamy (2008a)
reported a comparison of adaptive, non-adaptive,
and Lyapunov based clipped optimal strategies for
a nonlinear base isolated benchmark building. One
main disadvantage of the intelligent controllers is
that they are mostly problem oriented, and there-
fore a more general approach to voltage monitoring
still remains unexplored. Furthermore, neither the
intelligent controllers nor the model based clipped
optimal controllers consider the effect of the input
voltage on the commanded voltage dynamics (the
voltage that actually goes to the coil to create a
magnetic flux). The dynamics matters less when
the supplied voltage is a constant and does not vary.
When the supplied voltage to the MR damper is
varied based on the system responses and desired
performance of the system, the difference in the
supplied voltage and the commanded voltage
plays a crucial role.
This chapters provides details of an optimal
fuzzy logic control, a clipped optimal control, a
dynamic inversion control and integrator back-
stepping based control for MR damper monitor-
ing. Next section provides details of MR damper
modeling.
MAGNETORHEOLOGICAL DAMPERS
An MR damper consists of a hydraulic cylinder
containing MR fluid that, in the presence of a
magnetic field, can reversibly change from a
free-flowing, linear viscous fluid to a semi-solid
with controllable yield strength in a fraction of
a second (Ali and Ramaswamy 2009b; Wang
and Liao, 2011). An MR fluid is a suspension
of micron-sized magnetically soft particles in a
carrier liquid (such as water, mineral or synthetic
oil), that exhibits dramatic changes in rheologi-
cal properties. Under the influence of a magnetic
field these particles arrange themselves to form
very strong chains of fluxes (Yang et al., 2004;
Wereley and Pang, 1998). Once aligned in this
manner, the particles are restrained from moving
away from their respective flux lines and act as
a barrier preventing the flow of the carrier fluid.
A RD-1005-3 MR damper, manufactured by
Lord Corporation, which is used for the experi-
mental and numerical studies, is discussed here.
The damper is 208mm long in its extended posi-
tion, and provides a stroke of ± 25 mm. The input
voltage can be varied to a maximum of 2.5V
(continuous supply) and 5V (intermittent supply).
A simple Bouc-Wen model, developed by Spen-
cer et al. (1997) has been explored to characterize
the MR damper. The force f t
c ( ) provided by an
MR damper as predicted by the Bouc-Wen
model is given by
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