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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Acknowledgment
This research was supported by Unmanned Vehicles Advanced Core Technology Research and Development Program through the National Research Foundation of Korea (NRF), Unmanned Vehicle Advanced Research Center (UVARC) funded by the Ministry of Science, ICT and Future Planning, the Republic of Korea (No. 2016M1B3A1A01937245) and by the Ministry of Trade, Industry and Energy (MOTIE) under the R&D program (Educating Future-Car R&D Expert) (N0002428). It was also supported by Development Program through the National Research Foundation of Korea (NRF) (No. 2016R1D1A1B03935238).
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Doukhi, O., Fayjie, A.R., Lee, DJ. (2019). Supervisory Control of a Multirotor Drone Using On-Line Sequential Extreme Learning Machine. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-01054-6_64
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DOI: https://doi.org/10.1007/978-3-030-01054-6_64
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