neobjavljeni prilog sa skupa
This paper explores reinforcement learning (RL) for controlling the speed of DC motors. RL, widely successful
in areas like electrical grid management, is particularly beneficial in industrial automation, where improved system response
quality significantly enhance productivity. Using MATLAB Simulink and Simscape, Twin-Delayed Deep Deterministic (TD3)
RL agents were trained and optimized with Bayesian techniques, outperforming traditional PI controllers in terms of settling
times and overshoot, despite minor steady-state errors. This research offers valuable insights for integrating RL into
advanced industrial motor control systems.
MATLAB & Simulink, reinforcement learning (RL), Bayesian optimization, DC motors, Twin-Delayed Deep Deterministic (TD3)
Razvojem virtualizacijskih tehnologija raste kompleksnost IT sustava, dok korisnici istovremeno zahtijevaju brži pristup resursima, napredne integracije i jednostavnost korištenja. Jedno od ključnih rješenja za ove izazove primjena je…
Ovaj rad istražuje i uspoređuje mogućnosti prosljeđivanja grafičkih kartica na Hyper-V i KVM hipervizorima prema virtualnim strojevima, detaljno opisujući metode, postupke i razlike u arhitekturi koje utječu na…
The ever-growing usage and adaptation of HTTP/3 in web traffic leverages Quick UDP Internet Connections (QUIC) as a transport protocol. QUIC is widely recognized for reduced latency, enhanced…
