Abstract
Neural network-based controllers for vibration suppression of smart structural systems have been reported in several recent studies. These studies have shown that in addition to conventional controller design methodologies, neural networks offer an effective basis for design and implementation of controllers. With the introduction of neural network chips like the electronically trainable analog neural network (ETANN) chip i80170NX by Intel and the Ni1000 chip by Nestor Corp., stand-alone hardware implementation of neural network-based controllers is possible. In this paper the capabilities of Intel's ETANN chip to implement linear and nonlinear controllers for smart structural systems have been investigated. A neural network based optimizing controller design methodology that integrates the ETANN chip and its capabilities of on-line adaptation has been developed. A priori information on the smart structural systems such as actuator/sensor bandwidth limits and control effort limits can be directly accommodated in this method. Simulation studies of the performance of a closed loop time varying linear and nonlinear system have also been presented with and without on-line adaptation.
Recommended Citation
R. Damle and V. S. Rao, "Neural Network based Optimizing Controllers for Smart Structural Systems," Smart Materials and Structures, vol. 7, no. 1, pp. 23 - 30, IOP Publishing, Feb 1998.
The definitive version is available at https://doi.org/10.1088/0964-1726/7/1/004
Department(s)
Electrical and Computer Engineering
International Standard Serial Number (ISSN)
0964-1726
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 IOP Publishing, All rights reserved.
Publication Date
01 Feb 1998
