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.


Feedback Linearization and PID Control of Aero Thrust Pendulum using FPGA.

Aust. J. Basic & Appl.Sci, 2014, pp.466,472, Dec.

Schematic Representation of Aero Thrust Pendulum

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.

Global Humanitarian Technology Conference - South Asia Satellite (GHTC-SAS), 2014 IEEE vol., no., pp.183,188, 26-27 Sept

Switching States

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.

Leo Raju, Sibi Sankar, R.S. Milton, Procedia Computer Science, Volume 46, 2015, Pages 231-239, ISSN 1877-0509

multi-agent cq-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

Control, Embedded Systems and Robotics

Online Coursework
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
Credited Coursework
  • Data Structures and Algorithm EE2204
  • Transforms and Partial Differential Equations MA2211
  • Computer Networks CS2363
  • Object Oriented Programming CS2312
  • Operating System CS2411


Online Coursework
Credited Coursework
  • Presentation Skills and Technical Seminar EE2357
  • Professional Ethics in Engineering GE2025