A New Online Learned Interval Type-3 Fuzzy Control System for Solar Energy Management Systems

In this article, a novel method based on interval type-3 fuzzy logic systems (IT3-FLSs) and an online learning approach is designed for power control and battery charge planing for photovoltaic (PV)/battery hybrid systems. Unlike the other methods, the dynamics of battery, PV and boost converters are considered to be fully unknown. Also, the effects of variation of temperature, radiation, and output load are taken into account. The robustness and the asymptotic stability of the proposed method is analyzed by the Lyapunov/LaSalle's invariant set theorems, and the tuning rules are extracted for IT3-FLS. Also, the upper bound of approximation error (AE) is approximated, and then a new compensator is designed to deal with the effects of dynamic AEs. The superiority of the proposed method is examined in several conditions and is compared with some other well-known methods. It is shown that the schemed method results in high performance under difficult conditions such as variation of temperature and radiation and abruptly changing in the output load.