Geology Reference
In-Depth Information
•
Model based semi-active control algo-
rithms are very sensitive to the accuracy
of the mathematical model of the structure.
The proposed model based algorithms
should be supported with procedures to
identify structural parameters. This re-
mains to be further investigated using
adaptive backstepping, fuzzy backstep-
ping, etc. Robust backstepping technique
may be further explored to minimize the
sensitivity of the model based algorithms
to noise.
Agrawal, A. K., Tan, P., Nagarajaiah, S., & Zhang,
J. (2009). Benchmark structural control problem
for a seismically excited highway bridge—Part
I: Phase I problem definition.
Structural Control
and Health Monitoring, Special Issue
,
16
(5),
509-529. doi:10.1002/stc.301
Ahlawat, A. S., & Ramaswamy, A. (2004). Multi-
objective optimal fuzzy logic controller driven
active and hybrid control systems for seismically
excited nonlinear buildings.
Journal of Engineer-
ing Mechanics
,
130
(4), 416-423. doi:10.1061/
(ASCE)0733-9399(2004)130:4(416)
•
Along with the mathematical models for
the structures, neuro based training algo-
rithms should be supplemented to consider
the uncertainty in modeling arising out
of the flexibility at connections, effect of
nonlinearity (material and geometric), etc.
Thereafter the controller should be de-
signed on these hybrid models.
Ali, S. F., & Padhi, R. (2009). Active vibration
suppression of nonlinear beams using optimal dy-
namic inversion.
Journal of Systems and Control
Engineering
,
223
(5), 657-672.
Ali, S. F., & Ramaswamy, A. (2007). Develop-
ments in structural optimization and applications to
intelligent structural vibration control. In Lagaros,
N., & Tsompanakis, Y. (Eds.),
Intelligent compu-
tational paradigms in earthquake engineering
(pp.
125-247). Hershey, PA: Idea Group Publishing.
doi:10.4018/978-1-59904-099-8.ch006
•
Powering active/semi-active devices is
concern to engineers. Although semi active
devices operate at battery power, mainte-
nance and mounting of sensors and control
devices at remote locations provides chal-
lenge and are not cost effective. Therefore,
coming up with self powered, less energy
consuming devices and sensors remains an
effective choice in future. In this context
one can design self powered MR dampers,
which will be cost effective and environ-
mental friendly.
Ali, S. F., & Ramaswamy, A. (2008). GA opti-
mized FLC driven semi-active control for phase
II smart nonlinear base isolated benchmark build-
ing.
Structural Control and Health Monitoring
,
15
, 797-820. doi:10.1002/stc.272
Ali, S. F., & Ramaswamy, A. (2009a). Optimal
fuzzy logic control for MDOF structural systems
using evolutionary algorithms.
Engineering Appli-
cations of Artificial Intelligence
,
22
(3), 407-419.
doi:10.1016/j.engappai.2008.09.004
REFERENCES
Agrawal, A. K., & Nagarajaiah, S. (2009). Bench-
mark structural control problem for a seismically
excited highway bridge—Part I and II.
Structural
Control and Health Monitoring, Special Issue
,
16
(5), 503-528. doi:10.1002/stc.336
Ali, S. F., & Ramaswamy, A. (2009b). Testing and
modeling of MR damper and its application to
SDOF systems using integral backstepping tech-
nique.
Journal of Dynamic Systems, Measurement,
and Control
,
131
, 021009. doi:10.1115/1.3072154
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