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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 paper develops a neural network-based discrete predictive direct torque and flux control (NNPDTFC) for induction motor drive with the space vector modulation (SVM) technique. Moreover, this SVM technique with NNPDTFC is implemented to activate the inverter in the two-level operation and the performance is compared with the conventional PI direct torque and flux control (PIDTFC) technique. The PSO based model predictive control incorporated with the neural network is developed here in the NNPDTFC and is analyzed using MATLAB software. Disturbance reduction, simple control, and real-time implementation are the major features of NNPDTFC and it also enhances the transient performance of the motor drive by reducing settling time and peak overshoot. In addition, the flux and the torque ripples are significantly improved using the proposed NNPDTFC technique which is extensively used for the fast dynamic response of the induction motor drives as compared to PIDTFC. In order to show the potentiality of the proposed controller, a prototype controller is developed and validated with the laboratory setup and the control signals are generated for both NNPDTFC and PIDTFC using a low-cost Digital signal processor (DSP) controller which is fed to the induction motor of 3.7 kW capacity in the real-time platform. It is observed that the results with NNPDTFC are not only found to be extremely satisfactory even with the system and parameter uncertainties and external load perturbations but also, it produces enhanced dynamic as well as steady-state performance along with the reduced ripples in the signal flux, torque, and current compared to that of PIDTFC. • The neural-based predictive intelligence for DTFC of induction motor drive. • Enhances the system performance even with the model and parameter uncertainties, and load perturbations. • The stability is investigated and the robustness is also verified to show superiority. • DC voltage regulation without any additional controller even with the variations in parameters. • Finally, the system response is investigated and verified experimentally. [ABSTRACT FROM AUTHOR]