Randomized Self-Structuring Adaptive Neuro-Fuzzy Based Induction Motor Drives with Optimized FOPI Gains

Abstract

This paper describes the randomized evolving Takagi-Sugeno (ReTSK)-adaptive neuro-fuzzy (ANF) estimation algorithm and optimized fractional-order proportional integral (FOPI) controller are integrated with parameter adaptive indirect vector control (PA-IVC) for induction motor drives performance enhancement. For appropriate slip-speed tuning and field orientation, the machine learning-based ReTSK-ANF approach is proposed for the estimation of induction motor parameters and sensorless speed. The optimized FOPI speed and current regulators are employed in PA-IVC to generate the reference signals with minimized error for encountering manual tuning and reduce the overshoot with less settling time. A metaheuristic algorithm of Gazelle optimization algorithm (GOA) is imposed, to obtain the optimal weight, biases, membership functions (MFs), and MF rules in the predicative model of ReTSK-ANF for desired parameter estimation and optimal gains of FOPI for performance enhancement. Statistical metrics are carried out to examine the performance of ReTSK forecasting model. The metrics are mean square error (MSE), root mean square error (RMSE), mean error (ME), and error of standard deviation (ESD) as reportd during the training stage, were 3.33e-3, 3.41e-2, 1.92e-3, 3.37e-2, and during the testing stage 3.67e-3, 3.47e-2, 1.91e-3, 3.42e-2. This will confirm that the ReTSK-ANF estimator will achieve significant improvement in the estimation of parameters and closely follow the reference. Meanwhile, the optimized FOPI gains performance is analyzed using time response analysis.