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ABSTRACTThis research paper introduces an outline of a simplified version of single-input neuro-fuzzy controller (NFC) method via a decoupling-controlled intuitive feedback linearization (FBL) approach of induction motor (IM) model. The proposed NFC with FBL extensively reduces the torque and speed ripple along with better performance enhancement. A simplified state-space feedback linearization technique-based nonlinear IM model is designed and simulated in the stationary reference frame. Again, the parameter and plant uncertainties in the feedback-linearized model of the IM are continuous phenomenon which led to the design of a robust, simplified NFC to overcome these issues in the real-time industrial application. The proposed reduced membership function (MF) based simplified NFC is the integration of fuzzy logic and neural network concept with single input as an error (speed and torque) unlike two inputs error and change in error of conventional NFC. This has the advantage of improving the computational efficiency due to its simple structure and robustness over conventional NFC. This makes the system easy and simple to implement in a realistic situation. The performance and effectiveness of the proposed method using FBL concept of IM drive is analyzed using MATLAB software in different modes of operation and is distinguished with the conventional PI-controller and conventional NFC to show its superiority. The proposed system with the different control strategies is also validated by extensive experimental results using DSP 2812.

ABSTRACTThis paper employs an intuitive feedback linearization (FBL) controlled induction motor (IM) drive incorporated with the modified neuro-fuzzy sliding-mode control (NFSMC) based speed and torque compensators. The proposed method substantially reduces the torque and speed chattering with the optimal drive performance under system uncertainties and disturbance. An intuitive feedback technique-based linearization of IM is designed and simulated in the stationary reference frame. The modified neuro-fuzzy control (NFC) has the benefit of enhanced computational efficiency over classical NFC as the single input introduced here as an error instead of two inputs error and change in error as in classical NFC. Therefore, it is suitable for real-time applications. The parameters of the simple modified NFC is updated by an adaptive mechanism based on sliding-mode control (SMC). The effectiveness of the proposed method using FBL-based IM drive is verified in simulation as well as experiment and is evident from the comparative results that the system performance is not declined using proposed single-input simple NFSMC as compared to the conventional two-input NFSMC, rather it shows enhanced performance over PI-controller based drive. Extensive experimental results have been provided to justify the potentiality of the proposed techniques using DSP2812.