<p>This paper presents a closed loop Model Reference Adaptive system (MRAS) observer with artificial intelligent Nuero fuzzy controller (NFC) as the adaptation technique to mitigate the low speed estimation issues and to improvise the performance of the Sensorless Direct Torque Controlled (DTC) Induction Motor Drives (IMD). Rotor flux MRAS and reactive power MRAS with NFC is explored and detailed analysis is carried out for low speed estimation. Comparative analysis between rotor flux MRAS and reactive power MRAS with PI as well as NFC as adaptive controller is performed and results are presented in this paper. The comparative analysis among these four speed estimation methods shows that reactive power MRAS with NFC as adaptation mechanism shows reduced speed estimation error and actual speed error at steady state operating conditions when the drive is subjected to low speed operation. Simulation carried out using MATLAB-Simulink software to validate the performance of the drive especially at low speeds with rated and variable load conditions.</p>

Sensorless Direct Torque Control (DTC) is a powerful control scheme for high performance control of induction motor (IM) drives, which provides very quick dynamic response with simple structure and a decoupled control of torque and flux. The performance of the DTC drive depends greatly on the accuracy of the estimated flux components, torque and speed, using monitored stator voltages and currents. Low speed estimation is a great challenge because of the presence of transient offset, drift and domination of ohmic voltage drop.Extended Kalman filter (EKF) is a non linear adaptive filter which performs the process of finding the best estimate from the noisy data based on state space techniques and recursive algorithm.This paper mainly focuses on the accurate estimation of speed ranging from very low speed to rated speed using the equation of motion. A new state space model of the IM is developed for estimation in EKF, with load torque as an input variable and not as an estimated quantity which is the case in most previous studies.The developed algorithm is validated using MATLAB-Simulink platform for speeds ranging from low speed to rated speed at rated torque and at various torque conditions. An exhaustive analysis is carried out to validate the performance of DTC Induction motor drive especially at the low speeds. The results are promising for accurate estimation of speed ranging from low speed to rated speed using EKF.

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In the digital world, Static Random Access Memory (SRAM) is one of the efficient core component for electronics design, it consumes huge amount of power and die area. In this research, the SRAM design analysis in terms of read margin, write margin and Static Noise Margin (SNM) for low power application is considered. In SRAM memory, both read and write operation affect by noise margin. So, read and write noise margins are considered as the significant challenges in designing SRAM cell. In this research, robust 6T-SRAM cell is designed to decrease the power utilization. The Auto Awake Mode is developed to control the entire 6T-SRAM cell design. The proposed 6T-SRAM- Auto Awake Mode (6T-SRAM-AAM) was implemented to reduce power utilization of understand and write down operation inside the 20 nm FinFET library. The experimental results showed the proposed 6T-SRAM-AAM design reduced power consumption of read & write operation up to 25% to 33.33% compared to existing Static RAM cells design

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