Figure 2.

Real-time closed-loop energy regulation. The offline process includes extending the cortisol data profiles for multi-day data creation, simulating the medication dynamics, and estimating the filter parameters required for the closed-loop section. In the real-time closed-loop system, using the state-space representation, the internal energy state gets related to the brain secretion times that result in the cortisol secretions. Then, a Bayesian decoder employs the brain secretion times and the upper and lower cortisol envelopes as the observations to estimate the energy state. Finally, employing the personalized desired energy levels and information regarding the actuation dynamics, a fuzzy controller is generated. The closed-loop system automates the time and dosage of applying simulated medications to regulate the energy state.