Abstract
Quadrotors are underactuated nonlinear systems, which mean it needs online monitoring and controller tuning during flight period. Classical proportional integral derivative (PID) control and artificial neural network control (NNC) shows good results in many applications. Therefore in this paper, we propose a neural network supervisory control technique for the classical PID controller using fast online sequential learning method called on-line sequential extreme learning machine (OS-ELM). This technique seeks to online tune the control input to improve the flight capabilities automatically. The effectiveness of the proposed control algorithm comparing it with the conventional neural network based PID controller is demonstrated through realistic simulation using ROS-Gazebo framework.
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