  This paper proposes a robust control design method using
reinforcement-learning for controlling partially-unknown dynamical systems
under uncertain conditions. The method extends the optimal
reinforcement-learning algorithm with a new learning technique that is based on
the robust control theory. By learning from the data, the algorithm proposed
actions that guarantees the stability of the closed loop system within the
uncertainties estimated from the data. Control policies are calculated by
solving a set of linear matrix inequalities. The controller was evaluated using
simulations on a blood glucose model for patients with type-1 diabetes.
Simulation results show that the proposed methodology is capable of safely
regulates the blood glucose within a healthy level under the influence of
measurement and process noises. The controller has also significantly reduced
the post-meal fluctuation of the blood glucose. A comparison between the
proposed algorithm and the existing optimal reinforcement learning algorithm
shows the improved robustness of the closed loop system using our method.
