Adaptive Neuro-Fuzzy Inference System based Sliding Mode Control in the Presence of External Disturbances and Parameter Variation for Quadcopter UAV.
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Date
2025-04
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Addis Ababa University
Abstract
Quadrotor UAVs become more and more essential for surveillance, military and defense,
crop spraying, rescue missions, and to assess areas of destruction that are too dangerous
for humans to be engaged. But quadrotor is challenging in its control mechanism
because of its underactuated and coupled system due to these reasons different control
methods have been a focus of many researches.
It is shown through review of the literature that When it comes to handling nonlinearity,
underactuation, and coupled systems, sliding mode control (SMC) offers numerous
benefits over alternative control techniques. The Adaptive Neuro Fuzzy Inference
System (ANFIS), an intelligent controller, can minimize the chattering phenomena that
may affect system performance. In this work, Adaptive Neuro Fuzzy Inference System
based sliding mode control (ANFIS-SMC) is proposed to tackle the issue and to improve
the trajectory tracking performance. Combining the ANFIS with the SMC technology
proposes the intelligent robust controller scheme that is composed of controllers, which
includes position, altitude and attitude which enables tracking control of quadcopters,
which depends on the quality of the training data i.e the error input, and the output
of the SMC control signals, then the ANFIS based SMC controller to be able to adapt
changes by minimizing the chattering effect.
To validate the performance of the proposed ANFIS tuning SMC Controller, a comparison
is illustrated between the conventional Sliding mode controller with proposed
SMC based ANFIS controller using same system. From the result, ANFIS-SMC controller
remove the chattering and gives better performance compared with conventional
SMC methods developed in the study. The proposed control scheme is verified by developing
simulation results for the quadcopter using MATLAB/SIMULINK software.
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Keywords
Quad copter, Adaptive Neuro Fuzzy Inference System (ANFIS), Sliding Mode Control (SMC), Unmanned Aerial Vehicle (UAV), Trajectory tracking