This paper presents an adaptive neuro-fuzzy PID controller based on twin delayed deep
deterministic policy gradient (TD3) algorithm for nonlinear systems. In this approach, the …

In this paper, an online optimization approach of a fractional-order PID controller based on a
fractional-order actor-critic algorithm (FOPID-FOAC) is proposed. The proposed FOPID …

Reinforcement learning has recently been studied in various fields and also used to
optimally control IoT devices supporting the expansion of Internet connection beyond the …

Reinforcement learning has recently been studied in various fields and also used to
optimally control real devices (eg, robotic arms). In this paper, we try to allow multiple …

As the title suggests, in this work, a modern machine learning method called the Q-
fractionalism reasoning is introduced. The proposed method is founded upon a synergy of …

Federated reinforcement learning (FRL) has recently received a lot of attention in various
fields. In FRL systems, the concept of performing more proper actions with better …

In recent years, due to the development of technologies for unmanned aerial vehicles
(UAVs), also known as drones, UAVs have developed rapidly. Because of UAVs' high …

This letter proposes a data-driven hybrid deep learning method for dynamic total transfer
capability (TTC) control. It leverages deep learning (DL) to achieve fast prediction of TTC …

Supported by the advances in rocket technology, companies like SpaceX and Amazon
competitively have entered the satellite Internet business. These companies said that they …

The conventional dynamic inertia emulation scheme can desirably prolong the frequency
deflection period, sacrificing the frequency restraining time in the microgrid. Therefore, to …