M.Tech in Microelectronics and Control Systems from NMAMIT, Nitte under VTU.
RESEARCH INTERESTS
Aircraft Control, Electric Vehicle
8
Scopus Publications
Scopus Publications
IA2UCS: An Intelligent Atmospheric-Adaptive UAV Control System Using Machine Learning and Real-Time Weather Integration for Enhanced Flight Stability Divyesh Divakar, Rajalakshmi Samaga B L Advanced Control for Applications Engineering and Industrial Systems, 2026 Conventionally, Unmanned Aerial Vehicle (UAV) control systems are not able to handle atmospheric uncertainty such as wind turbulence, precipitation‐induced aerodynamic changes, and temperature changes, which degrade flight stability and mission reliability. Intending to introduce an Intelligence Atmospheric‐Adaptive UAV Control System (IA2UCS), this research uses Machine Learning (ML) algorithms with atmospheric data to improve the UAV performance in dynamic environmental conditions. These limitations are overcome in the proposed IA2UCS framework by the following three interconnected modules: (1) Robust Adaptive Control Module (RACM) that uses sliding mode control with Deep Reinforcement Learning (DRL) adaptation to deal with lumped uncertainties, (2) Predictive Atmospheric Intelligence System (PAIS) that uses ensemble learning algorithms, such as Random Forest, Long Short‐Term Memory (LSTM) networks, and Physics‐Informed Neural Networks (PINNs) to predict the weather, and (3) Flight Safety Assessment Protocol (FSAP) that uses fuzzy logic‐based risk evaluation to determine the flight conditions. The approach is to include real‐time sensor fusion, historical meteorological data analysis and adaptive parameter tuning to maximize flight performance. The proposed system demonstrates high resilience to actuator faults, rapid fault detection under 0.5 s, and stable recovery within 2 s, making it suitable for real‐world deployment in complex atmospheric environments.
Safe Fly Prediction Model for Unmanned Aerial Vehicle Divyesh Divakar, Rajalakshmi Samaga B L, Kanmani, Ramesh E, Prathibha M Cosmic 2024 IEEE International Conference on Computing Semiconductor Mechatronics Intelligent Systems and Communications Proceedings, 2024 In tropical regions like India, the weather is highly variable and can change rapidly, creating challenging conditions for Unmanned Aerial Vehicle operations. The lack of accurate, real-time predictive tools for assessing Unmanned Aerial Vehicle safety in such environments exacerbates the risk of accidents. Moreover, as Unmanned Aerial Vehicle become more integrated into commercial and public services, the demand for reliable safety protocols becomes even more urgent. This study evaluated the performance of various machine learning algorithms in predicting flight safety based on meteorological data. The Safe Fly Prediction Model, which employs machine learning algorithms to forecast flight safety based on meteorological data, obtained excellent accuracy and shown its potential for real-world use.
Harnessing Machine Learning to Illuminate and Anticipate Determinants of Academic Excellence in Student Ramesh E, Divyesh Divakar, Kanmani Jayaprakash, Prathibha M, Radha E G Cosmic 2024 IEEE International Conference on Computing Semiconductor Mechatronics Intelligent Systems and Communications Proceedings, 2024 It is very important to analyze and predict student academic achievement in education. The study is focused on the data of secondary school students' achievement in two Portuguese schools, which is obtained from the UCI Machine Library. We propose machine learning algorithm to cluster and predict students grades to obtain correlations between other factors and their academic grades. The work shows that the machine learning algorithm is predicting the relations between other factors and academic performance. Hence, the faculty can prioritize the other activities based on the input from machine learning to improve academic performance.
Machine Learning in Academic Performance Prediction: Analyzing Attendance and Marks to Forecast Future Results Kanmani, Divyesh Divakar, Keshaveni N, Ramesh E, Prathibha M Cosmic 2024 IEEE International Conference on Computing Semiconductor Mechatronics Intelligent Systems and Communications Proceedings, 2024 In the realm of higher education, predicting student performance is crucial for enhancing educational outcomes and institutional efficiency. This project aims to develop a predictive model to forecast students upcoming semester performance based on their past academic records and attendance. This paper makes use of machine learning regression techniques, including Random Forest, Gradient Boosting, and Voting Regressor, to analyze students' scores and attendance data. The dataset comprises marks and attendance records for analytical, theoretical, and practical subjects, with target grades categorized into seven classes: A+, A, B+, B, C, P and F. To evaluate model performance, continuous predictions were discretized into these grade categories, allowing for the calculation of Precision, Recall, F1-Score, and the generation of confusion matrices. The results indicate the efficacy of different models in accurately predicting student grades. Visualizations such as bar charts and heatmaps were used to present and compare the performance metrics across various models and subject types. The insights from this project are expected to assist educators in identifying at-risk students and improving educational strategies, ultimately contributing to enhanced student success and institutional reputation.
Simulation of the International Standard Atmosphere for Flight Reference Divyesh Divakar, Rajalakshmi Samaga B L International Interdisciplinary Humanitarian Conference for Sustainability Iihc 2022 Proceedings, 2022 The steady-state, standardized representation of the earth's atmosphere from the surface to 1000 km is called the U.S. Standard Atmosphere, 1976. These standard atmospheric parameters play significant role in flight controller design. This study put an effort to model standard atmosphere using MATLAB SIMULINK to obtain temperature, pressure and density at a given altitude. The obtained results will be rigorously compared to the standard value that has been tabulated. Additionally, a Standard Atmosphere Monitoring System is suggested in this work, with the potential to become an improved pilot interface. The presented model can be used for further study in the area of designing and developing adaptive flight controllers.
Design of 4 bit 4 pulse finite state machine serial adder Floyd James Sequeira, Akshatha A Mallya, Srigowri Hebbar, Anjali Shetty B., Divyesh Divakar Proceedings of the 3rd IEEE International Conference on Advances in Electrical and Electronics Information Communication and Bio Informatics Aeeicb 2017, 2017 There are many ways of modeling the behavior of systems, and the use of state machines is one of the oldest and best known. The behavior of the system at a given point in time depends upon the current state and the input or events that occur at that time. For each state the system may be in, behavior is defined for each possible input or event. Here we design a FSM serial adder in SIMULINK.
