Real-Time Adaptive Control of Nonlinear Smart Structural Systems using Neural Networks


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. Apriori information of 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 has also been presented with and without on-line adaptation.


Electrical and Computer Engineering

International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2024 Society of Photo-optical Instrumentation Engineers, All rights reserved.

Publication Date

01 Dec 1997

This document is currently not available here.