Online Tuning of PID Controller Using a Multilayer Fuzzy Neural Network Design for Quadcopter Attitude Tracking Control

Frontiers in Neurorobotics

Daewon Park¹  Sung Kyung Hong²  Tien-Loc Le³  Nguyen Vu Quynh³  Ngo Kim Long³ 

[1] Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul, South Korea;Faculty of Mechanical and Aerospace, Sejong University, Seoul, South Korea;Faculty of Mechatronics and Electronics, Lac Hong University, Bien Hoa, Vietnam;

关键词: quadcopter attitude;    fuzzy neural network;    proportional-integral-derivative;    attitude tracking control;    fuzzy PID;   

DOI  :  10.3389/fnbot.2020.619350

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown