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All Journal International Journal of Applied Power Engineering (IJAPE) Journal of Applied Data Sciences
Prakash, S.
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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High speed BLDC motor for grid tied PV based EV system using hybrid PSO-spotted hyena optimized PI controller Prakash, S.; Boopathy, K.
International Journal of Applied Power Engineering (IJAPE) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v13.i3.pp768-782

Abstract

The rapid adoption of electric vehicle (EV) motors has recently raised numerous issues including high expensive, complex maintenance, and resonance problems. Some of the most effective and most thoroughly investigated EV motors are 3ф induction motors and DC motors. Brushless DC (BLDC) motors for EVs are a more advanced version of the solution used in developing nations. Rising time, steady state, transient, overshoot, settling time and other characteristics of the EV based BLDC motor are difficult to control. A loss of control leads to system instability and reduces the components' lifespan. Thereby, in this work, a grid incorporated PV fed EV based BLDC motor is proposed using DC-DC converter along with hybrid optimized PI controller. An innovative high gain Luo converter has been developed with the goal to deal with the fluctuating behavior of PV systems and it provides the impressive advantages of a high conversion range, reduced voltage stress and outstanding efficiency. To considerably improve the performance of the suggested converter, the reliable hybrid particle swarm-spotted hyena optimized (PS-SHO) proportional integral (PI) controller is invented for controlling the BLDC motor's speed. The grid supplies electricity to the BLDC motor when the PV-based power source isn't accessible. The simulation used to determine the efficacy of the proposed BLDC motor system in MATLAB has confirmed that the methodology provides increased efficacy with a highest efficiency of 97.3% and a lower total harmonic distortion (THD) of 2.02%.
IoT-Enabled Supervised Learning-Based Prediction Model for Smart Instrumentation Controllers in Signal Conditioning Systems Prakash, S.; Kalaiselvi, B; Batumalay, M.
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.803

Abstract

This study proposes an intelligent Machine Learning (ML)-based smart controller for industrial flow process systems to enhance accuracy, adaptability, and robustness compared to conventional Proportional–Integral–Derivative (PID) controllers. The main idea is to replace reactive PID tuning with a proactive data-driven control strategy capable of predicting deviations and adjusting process parameters in real time. The objective is to develop and evaluate supervised learning models that can replicate and improve PID performance using real-time operational data collected from a flow process station. The proposed system integrates Internet of Things (IoT) sensors and edge computing to continuously acquire and process flow rate, pressure, and valve position data for model training and testing within the WEKA platform. Four classifiers—Linear Regression, Multilayer Perceptron (MLP), Sequential Minimal Optimization Regression (SMOreg), and M5P model tree—were compared using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), and model-building time as key evaluation metrics. Experimental results demonstrated that the M5P pruned tree model achieved the best overall performance with an MSE of 0.0024, RMSE of 0.0577, and model-building time of only 0.03 seconds, outperforming Linear Regression (RMSE = 0.0028), MLP (RMSE = 0.026), and SMOreg (RMSE = 0.0279). The findings show that the M5P-based controller closely replicates PID behavior while offering superior predictive accuracy, faster computation, and self-adaptive learning capabilities. The novelty of this research lies in demonstrating that an IoT-enabled, data-driven smart controller can achieve real-time predictive control without requiring explicit mathematical models, thereby simplifying tuning complexities and paving the way for autonomous, scalable, and intelligent control systems in Industry 4.0 environments.
Co-Authors Batumalay, M. Boopathy, K. Kalaiselvi, B