This thesis presents an initial investigation into Active Damage Control (AD C) using
Artificial Intelligence (AI). AI can alleviate the sometimes complicated task of modelling
the system and also provides an adaptable solution process. The two research areas of
ADC, damage identification and damage control, are studied in separate investigations.
An AI technique called "rule induction" is used for the damage identification study.
Velocity data from three plates (one without damage, one with damage at the center, and
one with damage at the edge) are acquired using a laser data acquisition system. A set
of rules is then induced from these data which accurately identifies which plates have
damage and where the damage is located. With regard to the damage control, a real-time,
machine-learning technique called "BOXES" is used to locally control the vibration of various systems by identifying their vibrational patterns. Using this technique, it is shown
that the computer successfully learns an effective control law for various simulations
using its trials and failures as the only learning information. It is also seen that the
learning algorithm is somewhat less effective when experimentally applying this method
to a pin-pin, aluminum beam. A discussion of possible improvements are presented in
the future work section.
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