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Exciting winding inductance in doubly salient Switched Reluctance Motor drive has been maximized by the trend of aligned rotor position to produce the required torque. The SRM has many inbuilt merits such as low manufacturing cost, simplicity, robustness, high speed, efficiency and starting torque and makes it a suitable choice for high performance and low-cost applications like flywheels and vacuum cleaners The sensorless speed of the switched reluctance motor is achieved accurately using stator flux estimation technique(Antoni Arias R et al., 2013). The least torque swell rate can be accomplished by differing the stator and rotor post grasp which in turn reduce the torque ripple in switched reluctance motor(AVeera Reddy et al.,2018).The high power density switched reluctance motors have been considered to be effective solutions for high-speed applications. Stator windings in SRM are concentrated and rotor with no brushes and windings. Speed control along with motor parameter operating point and noise is performed using an intelligent controller based on brain emotional learning (Behzad Mirzaeian et al.,2011).The torque ripple in the stwitched reluctance motor can be improved by using the destructive interferences technique(C.Labiod et al.,2018) The Speed of the Switched reluctance motor as well as torque ripple can also be controlled using non-dominated genetic algorithm ( Kalaivani L et al., 2013). The suitability of the SRM drive to operate both in 4 quadrants as well as its compatibility in hazardous conditions such as explosion-proof machinery, mining, traction, and domestic applications specify a wide range of publicity for SRM drives. The magnetic circuit inductance of the SRM drive is a nonlinear function of the rotor position and phase current. Hence the drive can be optimized and control using a precise magnetic circuit model The switched reluctance motor speed with parameter variation and external load disturbances can be achieved by using robust adaptive neural network controller (Cunhe Li et al.,2018). Important dynamics of SRM are incorporated employing a going for the complex model for physical motor simulation. The raw data that was accumulated is fuzzified using the Fuzzy Expert system is proposed to diagnose anemia(Javad Aramideh et al.,2014).A comparative study between the Intelligent controller and classical PI controller for speed control of SRM has been carried out to shown the speed response in terms of settling time (Kiruthika D et al.,2014).A smart traffic system can be achieved using density based clustering to decide the best route from source to destination(E.Vijay sekat et al.,2018). The time response of the incoming and the outgoing phases of instantaneous torque is different and the torque ripple in the reluctance drive can be minimized by the overlap angle during the commutation period(H.A.Maksoud .,2020).The speed control mechanism along with torque ripple to obtain a better settling time in torque, speed and current is determined by MOL algorithm (Nutan Saha et al.,2018).The high-performance speed control of switched reluctance motor drive is possible by implementing the current sharing method (Jia-JunWang,2018).A control technique to control the speed of SRM by continuous conduction mode using four parameters (H.Torkaman et al.,2019).A hybrid controller based PI tuning technique to control the speed of switched reluctance motor which is highly non-linear in nature (Hassan El-Sayed Ahmed Ibrahim et al.,2018).The maximum torque value at a minimum mass can be achieved by using the combination of FEM and seeker optimization algorithm(Mohammad Javad Navardi et al.,2014).The torque ripple in switched relcuctance motor drive can be minimized by using a novel phase current profiling method(Rajib Mikail et al.,2013). Machine Learning algorithms to make predictions to uncover the patterns among the attributes of data (Reddy, G. T et al.,2020).An ensemble-based machine learning algorithm was proposed to normalize the diabetic data set (Reddy, G. T et al.,2020).A hybrid whale optimization algorithm was illustrated to optimize energy utilization in the network (Maddikunta,P.K,2020).A wind-driven and firefly algorithm was proposed to determine the load balancing of the energy cloud (Swarna Priya et al.,2020).A Keysplitwatermark algorithm is proposed to obtain promising reports against cyber attacks (Iwendi C et al.,2020).A novel CoTEC scheme is proposed to minimize the data traffic in multi domain networks(Sun Jian et al.,2018).An improved controller based on the fuzzy logic technique for SRM drive is proposed in this paper, which is mostly applied to plants having complexity, where the exact mathematical model is tough to obtain or when the model is strictly nonlinear. The controller improves motor dynamic and its performances in terms of Speed regulation, Torque response, and Flux response. SRM used in this research has a geometric structure of eight stator poles and six-rotor poles with a power rating of 1kW. The proposed technique generated reference current variations, based on a minimized error from actual speed and reference speed using Neural Network Controller. The second one has the function of optimizing the torque based on the rotor position and selecting the phase.