The adaptive control of smart structures using neural networks

Abstract

The application of adaptive control algorithms for vibration suppression of smart structures is investigated in this paper. An accurate mathematical representation is not required in this approach. The controller adapts to the parameter variations of the structural system by updating the controller gains. When the desired performance of an unknown plant with respect to an input signal can be specified in the form of a linear or a non-linear differential equation, stable control can be achieved using model-reference adaptive control (MRAC) techniques. The conventional MRAC techniques have been successfully implemented on a smartstructure test article resulting in perfect model following. We have also investigated the design of neural-network-based adaptive control system for smart structures. Two neural networks, one for system identification and another for control purposes, are needed for the implementation of adaptive controilers. In this paper, a direct variable model identification technique using neural networks has been developed. An iterative inversion of a neural model of the forward dynamics of the plant h a s been utilized for the implementation of a neural controller. This algorithm generates a smooth control. We have also developed an adaptive neuron activation function for reducing the learning time of neural networks. The performance of the neurainetwork-based adaptive controllers has been verified using simulation studies.

On Model Updating Using Neural Networks

“…An increasingly popular architecture is the radial basis function (RBF) neural network. x 1x 2x (3) x (N)…”

“…The majority of research in this area uses the neural network as a "black box", and the results are mostly limited to computer simulations. A few successful experimental results in the area of vibration control have been published by Chen et al [2] and Rao et al [3]. To date, only one work, published by Szewczyk & Hajela [4], used a neural network to solve a problem similar to the model updating problem.…”

Analogue vibration cancellation by means of piezoelectric elements

“…These range from simple techniques such as constant gain or variable gain feedback (Crawley and de Luis, 1987) to control algorithms using neural networks (Rao et al, 1994) and fuzzy logic (Zeinoun an Khorrami, 1994). In this work constant gain feedback was used to control the cantilever's first two bending modes.…”

Adaptive Output Feedback Control of a Flexible Base Manipulator

“…This universal approximation property has been used in adaptive control [32][33][34][35], and its potential for controlling uncertain flexible systems has been illustrated with simulation and experimental studies [36][37][38]. Its applications for robot manipulators are addressed in a state feedback setting in [32,33,39].…”