Neural network approach to variable structure based adaptive tracking of SISO systems

This paper presents a novel approach to adaptive tracking control of linear SISO systems, which can solve the traditional model reference adaptive control (MRAC) problems. In this approach, a neural network universal approximator is included to furnish an online estimate of a function of the state and some signals relevant to the desired trajectory. The salient feature of the present work is that a rigorous proof via Lyapunov stability theory is provided. It is shown that the output error will fall into a residual set which can be made arbitrarily small.