Design and Comparative Analysis of Genetic Algorithm Tuned Fractional and Integer Order PI Controllers with Adaptive Neuro fuzzy Controller for Speed Control of Indirect Vector Controlled Induction Motor
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Date
2019-01
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Addis Ababa University
Abstract
This thesis presents design and comparative analysis of fractional order PI controller, integer order PI controller and an adaptive neurofuzzy controller trained by input output data from fractional order PI controller for indirect vector controlled induction motor. The parameters of the two PI controllers were genetically optimized using square of error as a fitness function. The proposed neurofuzzy controller trained with input and output data of fractional order PI controller incorporates fuzzy logic algorithm with a multilayer artificial neural network structure using hybrid learning algorithm. This improves the performance of induction motor drive. The fractional order model of induction motor has been also investigated using simulation results and it was inferred that optimized model of induction motor is an integer order model. The performance of adaptive neurofuzzy inference system controller, was compared with fractional and integer order PI controllers using MATLAB simulation results with different operating conditions. It was observed from the simulation results that by using ANFIS, FOPI, and IOPI controllers, for the reference speed of 50 rad/sec, the percentage peak overshoots were 0.496%,13.068% and 15.698% respectively. Thus, ANFIS shows dramatic decrease in overshoot. Also the speed reaches its desired set value at 0.15 second in ANFIS controlled IM drive. These show the effectiveness of the designed neurofuzzy controller and the designed neurofuzzy controller tries to speed up the performance of IM drive. On the other hand, FOPI controller showed better performance than IOPI controller for IM drive, this is because of FOPI controller has one additional parameter for tuning which is integration order.
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Keywords
Fractional order controller, Induction Motor, Indirect vector control, Adaptive Neurofuzzy Inference System, Artificial Neural Network, Fuzzy logic, Hybrid learning algorithm