Chala Merga (Asst. Prof.)Abemelek Getachew2025-06-192025-06-192024-09https://etd.aau.edu.et/handle/123456789/5612This paper presents the application of a rule-based Artificial Neuro Fuzzy Inference System( ANFIS) controller for closed loop Volts/Hertz(V/f) induction motor speed control. ANFIS controllers provide several advantages over conventional controllers,including economic feasibility, broader operational range, and easier tuning using natural language. Additionally,selforganizing fuzzy controllers can automatically refine an initial set of fuzzy rules. The proposed control architecture utilizes two normalized input signals-Speed error and its derivative-to generate the output frequency change. Membership functions and fuzzy rules are defined using the Fuzzy Inference System(FIS) editor in MATLAB. The control surface is analyzed to verify the relationship between inputs and outputs. The system is modeled in MATLAB/SIMULINK,and the performance of the proposed Neuro- Fuzzy Logic Controller is compared with that of a conventional Proportional-Integral(PI) controller. The controller is fine tuned through trial-and-error,followed by auto-tuning simulations. Simulation results demonstrate the effectiveness and superior performance of the proposed control approach in achieving precise speed regulation for the induction motor. Through simulation results produced with MATLAB/SIMULINK software, the efficacy of the suggested control approach is confirmed.en-USV/f induction motor speed controlArtificial Neuro Fuzzy Inference System (ANFIS)proportional integral derivative controllerFuzzy Logic(FLC)MATLAB/SIMULINKThesis