  In this work, we consider the adaptive nonlinear control problem for strict
feedback nonlinear systems, where the functions that determine the dynamics of
the system are completely unknown. We assume that certain upper bounds for the
functions $g_i$s of the system are known. The objective of the control design
is to design an adaptive controller that can adapt to changes in the unknown
functions that are even abrupt. We propose a novel backstepping memory
augmented NN (MANN) adaptive control method for the control of strict feedback
non-linear systems. Here, each NN, in the backstepping NN adaptive controller,
is augmented with an external working memory. The NN can write relevant
information to its working memory and later retrieve them to modify its output,
thus providing it with the capability to leverage past learned information
effectively and improve its speed of learning. We propose a specific design for
this external memory interface and show that the proposed control design
achieves bounded stability for the closed loop system. We also provide
substantial numerical evidence showing that the proposed memory augmentation
improves the speed of learning by a significant margin.
