Education

• Ph.D. in Control Theory, IIT Delhi, CGPA - 10/10 ,2025 (expected)

• Dual Degree B.Tech. - M.Tech. in Electrical Engineering, IIEST Shibpur, CGPA - 9.46/10 ,2020

Research experience

• Winter 2017: Research Assistant
  • IIEST Shibpur
  • Supervisor: Prof. Aparajita Sengupta, Department of Electrical Engineering
  • Applied Reinforcement Learning Control Algorithms to control a Hardware Continuum Robot (Soft Robot) via MATLAB. The original system was a complex system owing to its High DoF. My controller optimally controlled the system with better performance than the methods in the literature.
• Summer 2018: Research Assistant
  • Jadavpur University
  • Supervisor: Prof. Ranjit Kumar Barai, Department of Electrical Engineering
  • Fabricated an Intelligent Landmark Recognition Mobile Robot with all associated driver circuits along-with controller software written on Raspberry Pi
  • Implemented a Fuzzy-Proportional Integral Controller from scratch in Python for the Raspberry Pi Platform to detect and navigate towards objects of interest. The resulting controller effectively migrated towards placards placed in a room and showed robust performance with respect to camera noise.

Skills

• Programming Languages
  • Assembly
  • C
  • C++
  • JavaScript
  • LaTeX
  • MATLAB
  • Python
  • VHDL
• Frameworks
  • Pytorch
  • ROS
  • TensorFlow

Publications

Control of Single-Segment Continuum Robots: Reinforcement Learning vs. Neural Network based PID

Chattopadhyay, S., Bhattacherjee, S., Bandyopadhyay, S., Sengupta, A. and Bhaumik, S., 2018, March. Control of single-segment continuum robots: reinforcement learning vs. neural network based PID. In 2018 International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT) (pp. 222-226). IEEE.

Performance Analysis of Deep Q Networks and Advantage Actor Critic Algorithms in Designing Reinforcement Learning-based Self-tuning PID Controllers

Mukhopadhyay, R., Bandyopadhyay, S., Sutradhar, A. and Chattopadhyay, P., 2019, July. Performance Analysis of Deep Q Networks and Advantage Actor Critic Algorithms in Designing Reinforcement Learning-based Self-tuning PID Controllers. In 2019 IEEE Bombay Section Signature Conference (IBSSC) (pp. 1-6). IEEE.

Awards and Fellowships

• Prime Minister’s Research Fellowship (May 2021)
• Awarded “Recommendation with Commendation” in PMRF Annual Review, towards the research performed in April 2021-May 2022; one of only 10 people nation-wide for the Electrical Engineering, Electronics Engineering broad-area Link.

Review Experience

• Robotics and Autonomous Systems, Elsevier
• American Control Conference, IEEE
• Conference on Decision and Control, IEEE
• International Journal of Control, Taylor & Francis
• Transactions on Neural Networks and Learning Systems, IEEE
• Transactions on Aerospace and Electronic Systems, IEEE
• Automatica, Elsevier

TA Experience

Soft Computing, AI & NN Laboratory (EE-753/2)

Power System Lab (EE-651(a))

Power System Protection Lab (EE-851(a))

Adaptive and Learning Control (ELL802)

Control Engineering Laboratory (ELP225)

Robotics Technology (JRL302)

Volunteer Experience

• Student Volunteer for IEEE CDC 2021, Austin, TX, USA

Positions of Responsibility

• Mentored Under-Graduate Students for their Summer Research Internships (Summer 2019)