AUTHOR=Park Daewon , Le Tien-Loc , Quynh Nguyen Vu , Long Ngo Kim , Hong Sung Kyung TITLE=Online Tuning of PID Controller Using a Multilayer Fuzzy Neural Network Design for Quadcopter Attitude Tracking Control JOURNAL=Frontiers in Neurorobotics VOLUME=14 YEAR=2021 URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2020.619350 DOI=10.3389/fnbot.2020.619350 ISSN=1662-5218 ABSTRACT=

This study presents an online tuning proportional-integral-derivative (PID) controller using a multilayer fuzzy neural network design for quadcopter attitude control. PID controllers are simple but effective control methods. However, finding the suitable gain of a model-based controller is relatively complicated and time-consuming because it depends on external disturbances and the dynamic modeling of plants. Therefore, the development of a method for online tuning of quadcopter PID parameters may save time and effort, and better control performance can be achieved. In our controller design, a multilayer structure was provided to improve the learning ability and flexibility of a fuzzy neural network. Adaptation laws to update network parameters online were derived using the gradient descent method. Also, a Lyapunov analysis was provided to guarantee system stability. Finally, simulations concerning quadcopter attitude control were performed using a Gazebo robotics simulator in addition to a robot operating system (ROS), and their results were demonstrated.