Active Vibration Control in Structures Using Magnetostrictive Terfenol with Feedback and/or Neural Network Controllers

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Abstract
Experimental results are described in which a rod of magnetostrictive terfenol was used in the dual capacity of a passive structural support element and an active vibration control actu ator and artificial neural networks were used for the adaptive real-time control algorithm. Tests were performed on a three-legged table, where the terfenol actuators mentioned above are the table legs. For the table experiment, shaker vibrations generated in the ground and transmitted to the tabletop (via the legs) were attenuated by counter vibrations synthesized in the table leg actuators. The goal of this experiment was to maintain a quiescent tabletop in the presence of floor vibrations. Utilizing a proportional-integral derivative and a neural network controller, actuated forces were used to cancel applied disturbance forces. The neural network architecture identifies (learns) and adapts to the tabletop forced disturbance through a fast adaptation law known as Adaptive Back Propagation, generating the required counter vibration. The architecture and hence the control was designed to be modular so cross talk (coupling in the control signal) is minimized. This puts an extra burden on the controller to decouple the spillovers but maintains modularity, an important feature for large scale implementations. This article describes work in this area and demonstrates the ability to cancel disturbances from static to the hundred hertz frequency range.

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