Implementation of feedback-linearization-modelled induction motor drive through an adaptive simplified neuro-fuzzy approach

Abstract

A simple modified version of neuro-fuzzy controller (NFC) method based on single-input, reduced membership function in conjunction with an intuitive flux–speed decoupled feedback linearization (FBL) approach of induction motor (IM) model is presented in this paper. The proposed NFC with FBL remarkably suppresses the torque and speed ripple and shows improved performance. Further, the modified NFC is tuned by genetic algorithm (GA) approach for optimal performance of FBL-based IM drive. Moreover, the GA searches the optimal parameters of the simplified NFC in order to ensure the global convergence of error. The proposed simplified NFC integrates the concept of fuzzy logic and neural network structure like a conventional NFC, but it has the advantages of simplicity and improved computational efficiency over the conventional NFC as the single input introduced here is an error (speed and torque) instead of two inputs, error and change in error, as in the conventional NFC. This structure makes the proposed NFC robust and simple as compared with conventional NFC and thus, can be easily applied to real-time industry application. The proposed system incorporated with different control methods is also validated with extensive experimental results using DSP2812. The effectiveness of the proposed method using FBL of IM drive is investigated in simulation as well as in experiment with different working modes. It is evident from the comparative results that the system performance is not deteriorated using the proposed simple NFC as compared to the conventional NFC; rather, it shows superior performance over PI-controller-based drive.