  In this paper, a novel robust tracking control scheme for a general class of
discrete-time nonlinear systems affected by unknown bounded uncertainty is
presented. By solving a parameterized optimal tracking control problem subject
to the unknown nominal system and a suitable cost function, the resulting
optimal tracking control policy can ensure closed-loop stability by achieving a
sufficiently small tracking error for the original uncertain nonlinear system.
The computation of the optimal tracking controller is accomplished through the
derivation of a novel Q-function-based $\lambda$-Policy Iteration algorithm.
The proposed algorithm not only enjoys rigorous theoretical guarantees, but
also avoids technical weaknesses of conventional reinforcement learning
methods. By employing a data-driven, critic-only least squares implementation,
the performance of the proposed algorithm is evaluated to the problem of
fully-automated, insulin-based, closed-loop glucose control for patients
diagnosed with Type 1 and Type 2 Diabetes Mellitus. The U.S. FDA-accepted
DMMS.R simulator from the Epsilon Group is used to conduct a comprehensive in
silico clinical campaign on a rich set of virtual subjects under completely
unannounced meal and exercise settings. Simulation results underline the
superior glycaemic behavior achieved by the derived approach, as well as its
overall maturity for the design of highly-effective, closed-loop drug delivery
systems for personalized medicine.
