Industrial Control Engineering

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http://etd.aau.edu.et/handle/12345678/192

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Browsing Industrial Control Engineering by Subject "ANFIS"

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    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.
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    Design and Simulation of Speed Sensorless, FOC of Induction Motor Drive Using ANFIS Controller and ANN Estimator
    (Addis Ababa University, 2020-06) Biniam, Abera; Mengsha, Mamo (PhD)
    This thesis presents design and simulation of Artificial Neural Network solution for speed estimation including an ANFIS for control of IM drives. In the past years the research efforts in the field of IM control concentrated on the identification and observation of this highly nonlinear dynamic plant. Existing vector control methods require speed sensor for control and field orientation purpose, but their installation makes the drive system bulky, unreliable and expensive and installing them might not be feasible in some applications, such as motor drives in hostile environment or high-speed drives. In such cases speed is obtained from easily measurable stator quantities. Many speed sensorless techniques have been proposed to cope up with speed sensing problem. Developed speed estimation algorithms are more or less parameter dependent and/ or computationally time consuming. In this thesis, the proposed estimation method is based on ANN to obtain the speed signal. The conventional PI controller is replaced by an ANFIS which tunes the fuzzy inference system with hybrid learning algorithm. The ANN is used as estimator, trained by Levenberg- Marquardit algorithm. The data for training are obtained from conventional FOC simulations when the motor drive is working in closed loop at various values of speeds and loads for speed observation. The complete drive system is modeled using MATLAB®2019a. Finally, the drive results have been analyzed for both steady state and dynamic conditions such as of speed tracking capability, torque response quickness, low speed behavior, step response of drive with speed reversal and sensitivity to motor parameter uncertainty. The error of simulation result between actual and estimated speed have been less than 0.3% for transient response, 0.2% for speed tracking and 0.44% during low speed operation. It was observed from simulation results that by using PI and ANFIS controller, for the reference speed of 151rad/sec, the rise and settling time are improved by 0.0938 and 0.1289 seconds at full respectively also robust response is achieved with the latter controller.