  Health-aware control (HAC) has emerged as one of the domains where control
synthesis is sought based upon the failure prognostics of system/component or
the Remaining Useful Life (RUL) predictions of critical components. The fact
that mathematical dynamic (transition) models of RUL are rarely available,
makes it difficult for RUL information to be incorporated into the control
paradigm. A novel framework for health aware control is presented in this paper
where reinforcement learning based approach is used to learn an optimal control
policy in face of component degradation by integrating global system transition
data (generated by an analytical model that mimics the real system) and RUL
predictions. The RUL predictions generated at each step, is tracked to a
desired value of RUL. The latter is integrated within a cost function which is
maximized to learn the optimal control. The proposed method is studied using
simulation of a DC motor and shaft wear.
