Browsing by Author "Daniel, Arega"

Now showing 1 - 1 of 1
  • No Thumbnail Available
    Item
    Design and Compare Adaptive Neuro-Fuzzy Inference System (Anfis) With Genetic Algorithm (Ga) Tunned Pi Controller for Speed Control Of Vector Controlled Induction Motor Drive
    (Addis Ababa University, 2018-11) Daniel, Arega; Dereje, Shiferaw (PhD)
    Nowadays, vector controlled induction motor drives with variable speed applications are widely used in order to achieve good dynamic performance and wide speed control. The conventional speed controllers for vector control of induction motor drive suffer from the problem of stability; besides, these controllers such as PI/PID controllers show either steady state error or sluggish response to the agitation in reference setting or during load perturbation. In this thesis a new method of controlling technique based on the combination of Artificial Neural Network (ANN) and fuzzy logic (FL) is proposed to improve the speed control of indirect vector controlled induction motor drive. Indirect vector controlled induction motor with genetic algorithm (GA) optimized PI controller is developed and is replaced with adaptive neuro-fuzzy controller (ANFIS) to overcome the problem of overshoot occurred in PI controller and to obtain quick steady state response and better speed control. The proposed technique is implemented using MATLAB/Simulink. In this thesis, the speed, torque and stator current responses with GA based PI controller and proposed adaptive neuro-fuzzy controller are compared and found that the proposed ANFIS based controller showed increased dynamic performance. The proposed adaptive neuro-fuzzy controller is better in overshot which is 0.475% and that of PI controller is 14.368%, raise time and settling time.