Academia
Education is what other people do to me, learning is what you do to yourself.
– Joi Ito
I have completed my undergrad at SSN College of Engineering (2011-2015)
majoring in Electrical and Electronics.
Publications
Feedback Linearization and PID Control of Aero Thrust Pendulum using FPGA.
This paper presents a model based design and implementation of a closed loop PID
controller for Aero Thrust Pendulum. It was done using MATLAB - System Generator Toolbox, to
demonstrate the Direct Real Time Simulation and Implementation (DRTSI) scheme.
A method for live tuning of proportional, integral, derivative and system gain is validated and implemented to facilitate robust
tuning. This serves to highlight the effect of each component of the PID controller on
the response of the system for educational purposes. Also, Serial communication is established between FPGA and
MATLAB/Simulink to facilitate extensive logging of the system’s output and provides
visual representation of the system’s response for various gains.
Keywords - MATLAB – System Generator; FPGA-Serial; Aero Thrust pendulum; FPGA-ADC
Reinforcement learning for optimal energy management of a solar microgrid.
In an optimization based control approach for solar microgrid energy management, consumer as an agent continuously interacts with the environment and learns to take optimal actions autonomously to reduce the power consumption from grid. A model-free Reinforcement Learning algorithm, namely three-step-ahead Q-learning, is used to optimize the battery scheduling in dynamic environment of load and available solar power. Solar power and the load feed the reinforcement learning algorithm. Simulation results using real numerical data are presented for a reliability test of the system. The uncertainties in the solar power and the load are taken into account in the proposed control framework.
Keywords - Optimization; Q-learning; Reinforcement learning; Solar microgrid.
Distributed Optimization of Solar Micro-grid Using Multi Agent Reinforcement Learning.
In the distributed optimization of micro-grid, we consider grid connected solar micro-grid system which contains a local consumer, a solar photovoltaic system and a battery. The consumer as an agent continuously interacts with the environment and learns to take optimal actions. Each agent uses a model-free reinforcement learning algorithm, namely Q Learning, to optimize the battery scheduling in dynamic environment of load and available solar power. Multiple agents sense the states of the environment components and make collective decisions about how to respond to randomness in load, intermittent solar power using a Multi-Agent Reinforcement Learning algorithm, called Coordinated Q Learning (CQL). The goals of each agent are to increase the utility of the battery and solar power in order to achieve the long term objective of reducing the power consumption from grid.
Keywords - Solar micro-grid; Multi-agent Reinforcement Learning; CQ-learning; battery scheduling; optimization
Academics and Coursework
Cumulative GPA: 8.35/10
Computer Vision
Online Coursework
- Introduction to Computer Vision
Queensland University of Technology(QUT MOOC Honor Code Certificate) - Introduction to Parallel Programming
(Udacity Honor Code Certificate) - Fundementals of Digital Image and Video Processing
Northwestern University(Coursera Honor Code Certificate)
Control, Embedded Systems and Robotics
Online Coursework
- Embedded Systems - Shape the World
University of Austin,Texas(Edx Verified Certificate) - Autonomous Mobile Robots
ETH Zurich(Edx Honor Code Certificate) - Autonomous Navigation for Flying Robots
Technische Universität München (TUM) (Edx Honor Code Certificate) - Control of Mobile Robots
Georgia Institute of Technology(Coursera Honor Code Certificate) - Introduction to Power Electronics
University of Colarado Boulder(Coursera Honor Code Certificate) - Robot Mechanics and Control, Part I
Seoul National University(Coursera Honor Code Certificate)
Credited Coursework
- Solid State Drives EE2352
- Microprocessors and Microcontroller EE2354
- Power Electronics EE2301
- Electrical Machines I EE2302
- Electrical Machines II EE2251
- Control Systems EE2253
- Digital Logic Circuits EE2255
- Linear Integrated Circuits and Applications EE2254
- Measurement and Instrumentation EE2201
- Electronics Devices and Circuits EE2203
Signals and Systems
Credited Coursework
- Circuit Theory EE2151
- Electromagnetic Theory EE2202
- Power System Analysis EE2351
- Digital Signal Processing EC2314
- Tranmission and Distribution EE2303
- Communication Engineering EC2311
Artificial Intelligence, Algorithms and Mathematics
Online Coursework
- Introduction to Computational Thinking and Data Science
MIT(EDX Verified Certificate) - From Nand To Tetris
The Hebrew University of Jerusalem(Coursera Honorcode Certificate) - Artificial Intelligence Planning
University of Edinburgh(Coursera Honor Code Certificate) - Machine Learning
Stanford University (Coursera Honor Code Certificate) - Introduction to Computer Science and Programming Using Python
MIT(EDX Verified Certificate)
Credited Coursework
- Data Structures and Algorithm EE2204
- Transforms and Partial Differential Equations MA2211
- Computer Networks CS2363
- Object Oriented Programming CS2312
- Operating System CS2411
Humanities
Online Coursework
- Model Thinking
Michigan State University (Coursera Honor Code Certificate) - Critical Thinking in Global Challenges
University of Edinburgh(Coursera Honor Code Certificate)
Credited Coursework
- Presentation Skills and Technical Seminar EE2357
- Professional Ethics in Engineering GE2025