Pareto-Based Multi-objective Optimization for Fractional Order Speed Control of Induction Motor by Using Elman Neural Network

This paper presents Pareto-based multi-objective optimization for speed control of induction motor with fractional order proportional integral () controller. The aim of this study is to find optimum values of tuning parameters of by using Elman neural network (ENN) and Pareto-based multi-objective optimization. In this context, proportional gain , integral gain and the order of fractional integral are selected as tuning parameters while settling time and overshoot are chosen as objective functions. Firstly, experiments have been carried out to obtain training and test data. Then, ENN has been trained to construct mathematical model which is necessary for multi-objective optimization. In the next step, accuracy and reliability of ENN model are tested by using test data taken from experimental set-up. Finally, Pareto-based multi-objective optimization method has been used to find the optimum values of tuning parameters that minimize both and values. The different three conditions of the Pareto solution set are applied to the experimental set-up to verify the effectiveness of the proposed method. Results show that ENN is well modelled for induction motor and Pareto solution is an effective method to find optimal values of controller coefficients according to desired and values.