Prognostics-Driven Optimal Control for Equipment Performing in Uncertain Environment

This paper discusses the problem of optimal control for systems performing in uncertain environments, where little information is available regarding the system dynamics. A reinforcement learning approach is proposed to tackle the problem. A particular method to incorporate Prognostics and Health Management information derived on the system of interest is proposed to improve the reinforcement learning routine. The ideas behind reinforcement learning-based search for optimal control strategies are outlined. A numerical example illustrating the benefits of using PHM information is given.