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Faculty Directory Rajarshi Mukhopadhyay
Rajarshi Mukhopadhyay

Rajarshi Mukhopadhyay

Associate Professor

[email protected]
Vidwan Profile Google Scholar

Academic Information

Ph.D. (Thesis Submitted, IIEST, Shibpur, 2025)

Research Areas

Reinforcement Learning Learning-based Intelligent Control Modeling and Simulation of Complex Nonlinear Dynamic Systems Meta-heuristic optimization techniques and its applications in controller tuning.

Research Interests

1) Machine Learning-based data-driven modelling of nonlinear dynamical systems with special interest in Robotic Systems. 2) Learning-based Intelligent controller design for nonlinear dynamical systems. 3) Application of meta-heuristic optimization algorithms for controller tuning.

Courses Taught

Cybersecurity, AI & ML, Soft Computing, Control Systems, Sensors & Transducers

Positions Held

Associate Professor at Dept. of CST & CSIT, IEM, New Town, UEMK
Research Fellow (Ph.D.) at Dept. of EE, IIEST, Shibpur
Assistant Professor at Dept. of ECE, Dream Institute of Technology

Publications

1.R. Mukhopadhyay, A. Sutradhar, and P. Chattopadhyay, “A novel investigation on the effects of state and reward structure in designing deep reinforcement learning-based controller for nonlinear dynamical systems,” International Journal of Dynamics and Control, Mar. 2024, ISSN: 2195-2698. DOI: 10.1007/s40435-024-01407-6. 2. S. Mitra, R. Mukhopadhyay, and P. Chattopadhyay, “Pso-driven designing of robust and computation efficient 1d-cnn architecture for transmission line fault detection,” Ex- pert Systems with Applications, vol. 210, p. 118 178, 2022, ISSN: 0957-4174. DOI: https://doi.org/10.1016/j.eswa.2022.118178. 3. B. Chakraborty, R. Mukhopadhyay, and P. Chattopadhyay, “Multi-objective optimization for complex trajectory tracking of 6-dof robotic arm manipulators,” in Computational Intelligence in Pattern Recognition, A. K. Das, J. Nayak, B. Naik, S. Vimal, and D. Pelusi, Eds., Singapore: Springer Nature Singapore, 2022, pp. 497–510, ISBN: 978-981-19-3089-8. 4. R. Mukhopadhyay, S. Bandyopadhyay, A. Sutradhar, and P. Chattopadhyay, “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), 2019, pp. 1–6. DOI: 10.1109/IBSSC47189.2019.8973068. 5. R. Mukhopadhyay, R. Chaki, A. Sutradhar, and P. Chattopadhyay, “Model learning for robotic manipulators using recurrent neural networks,” in TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON), 2019, pp. 2251–2256. DOI:10.1109/TENCON.2019.8929622. 6. R. Sen, R. Mukhopadhyay, A. Sutradhar, D. Ganguly, and P. Chattopadhyay, “Design of a lyapunov based fuzzy controller for 3 dof modular robotic leg,” in 2019 Fifth Indian Control Conference (ICC), 2019, pp. 460–465. DOI: 10.1109/INDIANCC.2019.8715618. 7. R. Mukhopadhyay, P. S. Panigrahy, G. Misra, and P. Chattopadhyay, “Quasi 1d CNN-based fault diagnosis of induction motor drives,” in 2018 5th International Conference on Electric Power and Energy Conversion Systems (EPECS), 2018, pp. 1–5. DOI: 10.1109/EPECS.2018.8443552.