Adaptive Neuro-Fuzzy Inference System based Sliding Mode Control in the Presence of External Disturbances and Parameter Variation for Quadcopter UAV.
| dc.contributor.advisor | Lebsework Negash (PhD) | |
| dc.contributor.author | Daniel Fikadu | |
| dc.date.accessioned | 2025-07-21T13:41:28Z | |
| dc.date.available | 2025-07-21T13:41:28Z | |
| dc.date.issued | 2025-04 | |
| dc.description.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. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/5663 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Quad copter | |
| dc.subject | Adaptive Neuro Fuzzy Inference System (ANFIS) | |
| dc.subject | Sliding Mode Control (SMC) | |
| dc.subject | Unmanned Aerial Vehicle (UAV) | |
| dc.subject | Trajectory tracking | |
| dc.title | Adaptive Neuro-Fuzzy Inference System based Sliding Mode Control in the Presence of External Disturbances and Parameter Variation for Quadcopter UAV. | |
| dc.type | Thesis |