  We introduced a {\it working memory} augmented adaptive controller in our
recent work. The controller uses attention to read from and write to the
working memory. Attention allows the controller to read specific information
that is relevant and update its working memory with information based on its
relevance. The retrieved information is used to modify the final control input
computed by the controller. We showed that this modification speeds up
learning. In the above work, we used a soft-attention mechanism for the
adaptive controller. Controllers that use soft attention or hard attention
mechanisms are limited either because they can forget the information or fail
to shift attention when the information they are reading becomes less relevant.
We propose an attention mechanism that comprises of (i) a hard attention
mechanism and additionally (ii) an attention reallocation mechanism. The
attention reallocation enables the controller to reallocate attention to a
different location when the relevance of the location it is reading from
diminishes. The reallocation also ensures that the information stored in the
memory before the shift in attention is retained which can be lost in both soft
and hard attention mechanisms. We illustrate through detailed simulations of
various scenarios for two link robot and three link robot arm systems we
illustrate the effectiveness of the proposed attention mechanism.
