A self-learning TS-fuzzy system based on the C-means clustering technique for controlling the altitude of a hexacopter unmanned aerial vehicle

In this research, we develop a self-learning fuzzy logic autopilot for high-performance trajectory tracking of a hexacopter unmanned aerial vehicle. We employ non-linear mathematical models of the system, derived from first principles, to gain more accurate understanding of its dynamic behaviours. Accordingly, we design the TS-fuzzy autopilot of a hexacopter for its altitude loop using the C-means fuzzy clustering technique to control the height of the drone. This research serves as our preliminary study to investigate the feasibility of our fuzzy control system before we can implement it into practice. We perform a systematic comparative study to highlight the effectiveness of our control algorithm. We demonstrate the performance and robustness of the proposed control system in terms of its tracking error with respect to the performance of the conventional PID controller.