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| dc.contributor.author | Kifle, Abay | |
| dc.date.accessioned | 2022-12-31T07:21:12Z | |
| dc.date.available | 2022-12-31T07:21:12Z | |
| dc.date.issued | 2022-10 | |
| dc.identifier.uri | http://ir.bdu.edu.et/handle/123456789/14800 | |
| dc.description.abstract | In the early days of aviation, to maintain aircraft pitch to desired pitch angle, it requires the continuous attention of a pilot because of it adjusted manually. So, as a pilot fly for many hours of flight, the constant attention of pilot may lead to serious fatigue. And if an aircraft’s pitch is not properly calibrated, it may enter a stall state, resulting in poor flying quality and aviation disaster. So that aircraft should have an autopilot pitch controller. This thesis present design of an adaptive neuro-fuzzy controller trained by pair of input-output data taken from GA_PID controller for aircraft pitch control. The parameters of GA_PID controller were genetically optimized using integral time absolute error (ITAE) as an objective function. The proposed neuro-fuzzy controller trained with input and output data of GA_PID controller incorporates fuzzy logic algorithm with a multilayer artificial neural network structure using hybrid learning algorithm. This improve the performance and stability of pitch control of an aircraft. To evaluate the performance of the approach, comparative analysis has been done with in time domain analyses and MATLAB/simulink software is used to attain simulation of aircraft pitch system. Classical PID, in which the parameter Kp , Ki and Kd are tuned manual and GA_PID were chosen for comparison to verify the effectiveness of adaptive neurofuzzy controller. It was observed from the simulation results that by using ANFIS, PID, and GA_PID controllers, for the reference pitch angle of 0.2 rad (11:5 o ), the percentage peak overshoots were 0.0019%, 0.33% and 1.5% respectively. Thus, ANFIS shows decrease ( it has no) in overshoot. Also the aircraft reaches its desired pitch angle set value at 0.0954 second in ANFIS controlled aircraft pitch. These show the effectiveness of the designed neuro-fuzzy controller and the designed neuro-fuzzy controller tries to improve the performance and stability of aircraft pitch. Therefore, the comparison confirmed that, the better performance of the proposed adaptive neuro-fuzzy controller over other approach (mechanism) for controlling of the pitch angle of an aircraft. Keywords:- Aircraft pitch control, ANFIS, GA-PID, PID, longitudinal dynamics, and Matlab r /Simulink r . | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | ELECTRICAL AND COMPUTER ENGINEERING | en_US |
| dc.title | Adaptive Neuro-Fuzzy Controller for General Aviation Aircraft Pitch Control System | en_US |
| dc.type | Thesis | en_US |
