Adaptive output feedback control of a class of multi-input multi-output systems using neural networks

Abstract

For a class of uncertain multi-input multi-output non-linear systems an adaptive output feedback control methodology is developed using linearly parameterized neural networks. The neural network operates over a tapped delay line of memory units, comprised of system input/output signals. The adaptive laws for neural network parameters are written in terms of a linear observer of the nominal system's error dynamics. Ultimate boundedness of the error signals is shown through Lyapunov's direct method. Simulations illustrate the theoretical results.

Acknowledgement

This research has been sponsored under AFOSR contracts F49620-03-1-0443, F4960-01-1-0024.