Tootfinder

A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object tracking
Faraz Lotfi, Khalil Virji, Nicholas Dudek, Gregory Dudek
https://arXiv.org/abs/2401.16618

A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object tracking
In this paper, we present an exploration and assessment of employing a centralized deep Q-network (DQN) controller as a substitute for the prevalent use of PID controllers in the context of 6DOF swimming robots. Our primary focus centers on illustrating this transition with the specific case of underwater object tracking. DQN offers advantages such as data efficiency and off-policy learning, while remaining simpler to implement than other reinforcement learning methods. Given the absence of a d…

Data-Driven System Identification of Quadrotors Subject to Motor Delays
Jonas Eschmann, Dario Albani, Giuseppe Loianno
https://arxiv.org/abs/2404.07837 htt…

Data-Driven System Identification of Quadrotors Subject to Motor Delays
Recently non-linear control methods like Model Predictive Control (MPC) and Reinforcement Learning (RL) have attracted increased interest in the quadrotor control community. In contrast to classic control methods like cascaded PID controllers, MPC and RL heavily rely on an accurate model of the system dynamics. The process of quadrotor system identification is notoriously tedious and is often pursued with additional equipment like a thrust stand. Furthermore, low-level details like motor delays…