Park Daewon, Le Tien-Loc, Quynh Nguyen Vu, Long Ngo Kim, Hong Sung Kyung
Faculty of Mechanical and Aerospace, Sejong University, Seoul, South Korea.
Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul, South Korea.
Front Neurorobot. 2021 Jan 18;14:619350. doi: 10.3389/fnbot.2020.619350. eCollection 2020.
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.
本研究提出了一种基于多层模糊神经网络设计的在线整定比例-积分-微分（PID）控制器，用于四旋翼飞行器的姿态控制。PID控制器是简单但有效的控制方法。然而，寻找基于模型的控制器的合适增益相对复杂且耗时，因为它取决于外部干扰和被控对象的动态建模。因此，开发一种用于在线整定四旋翼飞行器PID参数的方法可以节省时间和精力，并能实现更好的控制性能。在我们的控制器设计中，提供了一种多层结构以提高模糊神经网络的学习能力和灵活性。使用梯度下降法推导出在线更新网络参数的自适应律。此外，还进行了李雅普诺夫分析以保证系统稳定性。最后，除了机器人操作系统（ROS）之外，还使用Gazebo机器人模拟器对四旋翼飞行器姿态控制进行了仿真，并展示了其结果。
