Recurrent Neural Networks for Robust Vibration Control of Composite Shells
A neural network-based control system is developed for self-adapting vibration control of laminated double curved shells with piezoelectric sensors and actuators. A hybrid control system developed in this paper is comprised of a feed-forward neural network identifier and a dynamic recurrent neural network controller. Sensing and actuation is achieved using piezoelectric sensors and actuators. Finite element equations of motion are developed based on shear deformation theory and implemented for the double curved laminated shell. The dynamic effects of the mass and the stiffness of the piezoelectric patches are considered in the model. The performance and robustness of the hybrid control system are examined using different initial conditions, loading and system parameter variations. The hybrid control system is shown to perform effectively in all of these cases.
Y. Shen et al., "Recurrent Neural Networks for Robust Vibration Control of Composite Shells," Intelligent Engineering Systems through Artificial Neural Networks, American Society of Mechanical Engineers (ASME), Jan 2000.
Mechanical and Aerospace Engineering
Electrical and Computer Engineering
Keywords and Phrases
Artificial Intelligence; Neural Networks
Article - Conference proceedings
© 2000 American Society of Mechanical Engineers (ASME), All rights reserved.
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