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This article describes a linearization-based model predictive intelligence control (MPIC) approach incorporated with a feedback linearization (FBL)-based direct torque and flux control (DTFC) of induction motor (IM) drive. This FBL DTFC with MPIC is employed here to analyze the dynamic performance of the IM drive and is compared with the conventional MPIC-based DTFC technique. The intuitive FBL approach develops the decoupled components of torque and flux. Hence, the specific intuitive linearized model-based DTFC-SVM not only preserves all advantages of DTFC but also improves the transient response with a reduced settling time of 30 to 40% along with a substantial reduction in torque and flux ripple by around 78% and 68%, respectively. However, the stability problem to design such a controller is a significant issue, and therefore, it is examined by estimating that the control law is monotonically converging with time. Hence, the proposed solution is highly justifiable and flexible. This proposed controller-based feedback linearized drive is modeled and its dynamic response and robust performance are analyzed by using MATLAB/SIMULINK software as well as extensive experimental results. It is observed from the experimental result that there is a significant improvement in torque and flux ripple of around 78% and 67%, respectively. [ABSTRACT FROM AUTHOR]

This 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. [ABSTRACT FROM AUTHOR]

This paper develops a sliding-mode based simplified structure of neuro-fuzzy speed and torque compensator incorporated with an induction motor (IM) drive deploying feedback linearization (FBL). The intuitive linearization technique with the proposed simplified structure neuro-fuzzy sliding-mode control (NFSMC) considerably improves the torque and speed responses under system uncertainty and outer load disturbance, giving optimal system performance. This proposed technique also has high computational efficiency due to single error input over conventional one and thus can easily be applied for industrial uses. The parameter tuning of the simplified neuro-fuzzy control (NFC) is done by sliding-mode control (SMC) based adaptive mechanism. The proposed simplified method based linearized drive is simulated as well as experimentally investigated using low-cost DSP2812. The responses prove that the drive system performance characteristics using proposed simplified NFSMC is well-preserved compared to that of conventional one. Additionally, it provides optimal dynamic performance and is robust in terms of parameter variations and peripheral load disturbance. [ABSTRACT FROM AUTHOR]