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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 article focuses on the design and implementation of a hybrid fuel cell supply system to deliver the power demanded by the feedback linearization (FBL) based induction motor (IM) drive. The proposed modified simple hybrid neuro-fuzzy sliding-mode control (NFSMC) with intuitive FBL substantially reduces torque chattering and improves speed response, giving optimal drive performance under system uncertainties and disturbances. This novel technique also has the benefit of reduced computational burdens by improving computational efficiency over conventional NFSMC and thus suitable for real-time industrial applications. The parameters of the modified neuro-fuzzy control are tuned by an adaptive mechanism based on sliding-mode control. A fuel cell stack with a dc/dc boost converter is considered here as a separate external source during interruption of main supply for maintaining the supply to the motor drive control through the inverter, thereby reducing the burden and average rating of the inverter. A rechargeable battery used as an energy storage supplements the fuel cell during different operating conditions of the drive system. The effectiveness of the proposed method using the fuel cell-based linearized IM drive is investigated in simulation, and the efficacy of the proposed controller is validated by experiment using DSP2812. It is evident from the results that the system provides optimal dynamic performance in terms of remarkably reduced torque ripple, good load disturbance rejection, and reduced speed settling time responses by around 92%, 50%, and 41%, respectively, compared to the PI-controller. The system is also robust in terms of parameter variation and external load. [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]

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]