Open AccessTiltrotors Position Tracking Controller Design Using Deep Reinforcement Learning
Author(s) -
Yujia Huo,
Yiping Li,
Xisheng Feng
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/751/1/012047
Subject(s) - reinforcement learning , position (finance) , robot , controller (irrigation) , tracking (education) , trajectory , computer science , control theory (sociology) , nonlinear system , control engineering , position tracking , artificial intelligence , simulation , engineering , control (management) , actuator , psychology , pedagogy , physics , finance , quantum mechanics , astronomy , agronomy , economics , biology
In this paper, a quad-tiltrotors air-water trans-domain robot is introduced. The nonlinear dynamic behaviours with uncertainties require a robust controller for multi-tasks. For this robot, controllers are designed using deep reinforcement learning method solving position and attitude control when operating as a UAV in the air. A ROS combining Gazebo simulation platform is designed to train the robot. The simulation results show the tiltrotors robot gets capabilities of spots tracking as a quad-rotors, and trajectory tracking as both the quad-rotors and tiltrotors.