the performance of the fuzzy controllers as compared
to a robust backstepping technique for nonlinear con-
trol in Figures 7, 8. Both Mamdani and Sugeno fuzzy
controllers are able to stabilize the quadrotor from
the critical initial conditions. The settling times are
slightly longer than the backstepping controller, spe-
cially for the ψ angle which can be improvedwith fur-
ther tuning of the FLC’s membership functions. How-
ever, this is overlooked because the fuzzy controllers
are stabilizing both position and attitude despite the
presence of strong wind conditions as high as 30m/s
and sensory noise of −55dB.
5 CONCLUSIONS
A fuzzy logic approach was proposed for the au-
tonomous control of quadrotors without the need for a
mathematical model of their complex and ill-defined
dynamics. The fuzzy technique was implemented
through Sugeno and Mamdani inference engines for
comparison. The controller comprises of six indi-
vidual fuzzy controllers designated for the control of
the quadrotor’s position and orientation. The investi-
gation on the two types of control methodologies is
conducted in a simulation environment, where distur-
bances such as wind conditions and sensor noise are
incorporated for a more realistic simulation. The re-
sults demonstrated a successful control performance
of the quadrotor with both Mamdani and Sugeno
fuzzy controllers despite the disturbances. When
compared with other control techniques presented
in the literature for the same purpose, the proposed
method showed a higher robustness and faster con-
vergence, where satisfactory attitude stabilization is
achieved despite stringent initial conditions and se-
vere disturbances. The future work will be directed
towards a real-world implementation of the proposed
fuzzy controllers. Furthermore, the online adaptation
of controllers will be investigated.
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