Sarangapani-Jagannathan
Jagannathan Sarangapani

William A. Rutledge - Emerson Electric Company Distinguished Professor

Electrical Engineering

Jagannathan Sarangapani is at the Missouri University of Science and Technology, Rolla, MO, USA, where he is a Rutledge-Emerson Distinguished Professor and was Site Director for the graduated NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems. He also has a courtesy appointment with the Department of Computer Science. He has co-authored 179 peer-reviewed journal articles, over 289 refereed IEEE conference articles, several book chapters, and co-authored four books and two edited books. He holds 21 patents, one defense publication, with several pending. He has supervised the completion of over 30 doctoral students and 31 M.S. thesis students. His research funding is in excess of $18.6 million dollars (his shared credit $10.57 million) from NSF, NASA, AFOSR, ARO, ONR, AFRL, Boeing, Honeywell, Sandia and from other companies. His current research interests include neural network learning, adaptation, decision making and control, networked control systems/cyber physical systems, prognostics/bigdata, and autonomous systems/robotics with healthcare applications.  He served/serving on various editorial boards and as a co-editor for the IET Book series on Control.

Journal Papers (recent)

  1. Tejalal Choudhary, Vipul Kumar Mishra, Anurag Goswami, Jagannathan Sarangapani, “A transfer learning with structured filter pruning approach for improved breast cancer classification on point-of-care devices”, Journal of Computers in Biology and Medicine, accepted for publication, April 2021.
  2. Raghavan, S. Jagannathan, and V. Samaranayake, "A game-theoretic approach for addressing domain-shift in big-data", IEEE Transactions on Bigdata, accepted for publication, April 2021.
  3. Moghadam, P. Natarajan, and S. Jagannathan, “Online optimal adaptive control of partially uncertain nonlinear discrete-time systems using multilayer neural networks”, IEEE Transactions on Neural Networks and Learning Systems, accepted for publication, February 2021.
  4. Jinna Li, Xiao, T. Chai, F.L. Lewis, and S. Jagannathan, " Adaptive interleaved reinforcement learning: robust stability of affine nonlinear systems with unknown uncertainty", IEEE Transactions on Neural Networks and Learning Systems, accepted for publication, October 2020.
  5. Prakash, L. Behera, S. Mohan, and S. Jagannathan, “Dual loop optimal control of a robot manipulator and its application in warehouse automation”, IEEE Transactions on Automation Science and Engineering, accepted for publication, September 2020.
  6. Raghavan, Shweta Garg, S. Jagannathan, and V. Samaranayake, "Distributed min-max learning scheme for neural network with applications to high dimensional classification", IEEE Transactions on Neural Networks and Learning Systems, accepted for publication, August 2020.
  7. Narayanan, H. Moderes, S. Jagannathan and F. L. Lewis, “Event-driven off-policy reinforcement learning for control of interconnected systems”, IEEE Transactions on Cybernetics, accepted for publication, April 2020.
  8. Haifeng Niu and S. Jagannathan, “Flow based attack detection and accommodation for networked control systems”, International Journal of Control, vol. 94, no. 3, 834-847, March 2021.
  9. Natarajan, R. Moghadam, and S. Jagannathan, “Online deep neural network-based feedback control of a Lutein bioprocess”, Journal of Process Control, vol. 98, pp. 41-51, 2021.
  10. Raghavan, S. Jagannathan, V. Samaranayake, “Direct error-driven learning for deep neural networks with applications to big-data”, IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 5, pp. 1763-1770, May 2020.

 

Conference Papers (representative)

  1. Rohollah Moghadam, and S. Jagannathan, “Optimal adaptive regulation of uncertain linear continuous-time systems with state and input delays”, of the IEEE Conference on Decision and Control, pp. 132-137, December 2020.
  2. Rohollah Moghadam, P. Rajan, and S. Jagannathan, “Multilayer neural network-based optimal adaptive tracking control of partially uncertain nonlinear discrete-time systems”, of the IEEE Conference on Decision and Control, pp. 2204-2209, December 2020.
  3. Jinna Li, Zhenfei Xiao, TianYou Chai, Frank L. Lewis, and S. Jagannathan, “Off-policy Q-learning for anti-interference control of multi-player systems”, Proc of the IFAC World Congress, Berlin Germany, July 2020.
  4. Rohollah Moghadam, Pappa Natarajan, Krishnan Raghavan and S. Jagannathan, “Online optimal adaptive control of a class of uncertain nonlinear discrete-time systems”, of the IEEE International Joint Conference on Neural Networks (IJCNN) as part of WCCI, pp. 1-6, August 2020.
  5. Moghadam and S. Jagannathan, “Optimal control of linear continuous-time systems in the presence of state and input delays with application to a chemical reactor”, Proc. of American Controls Conference, pp. 999-1004, July 2020.
  6. Moghadam and S. Jagannathan, “Approximate optimal adaptive control of partially unknown linear continuous-time systems with state delay”, Proc. of the IEEE Conference on Decision and Control, pp. 1985-1990, December 2019.

 

Books (edited) Published

  • K. Vamvoudakis and S. Jagannathan, “Control of Complex Systems: Recent Advances and Future Directions”, Wiley, (Edited) 2016.

Book Chapter(s)

  • Rohollah Moghadam, V, Narayanan, S. Jagannathan, and Krishnan Raghavan, “Optimal adaptive control of uncertain linear systems with time-delay”, Springer, in Handbook of Reinforcement Learning and Control, Editors: K.G. Vomvoudakis, Y. Wan, F. Lewis and D.Canseer, 2021.
  • Krishnan Raghavan, S. Jagannathan, and V. Samaranayake, “Direct error driven learning for classification with applications to Bigdata”, Editors: W. Pedrycz and S. Chen, Deep Learning Architectures, Springer Nature, pp. 1-30, 2020.
  • Hao Xu and S. Jagannathan, “Joint scheduling and event triggered optimal control design for cyber physical systems”, Editors:  Sandip Roy and Sajal Das, Principles of CPS: An Interdisciplinary Approach, Cambridge University Press, pp. 104-126, 2020.

Patents

  • Al Salour, D. Trimble, J. Sarangapani, and E. Taqieddin, "Ultra-lightweight Mutual Authentication Protocol with Substitution Operation”, US Patent No. 10198605, February 5, 2019. (jointly with Boeing, St Louis)

Recent Grants (active)

  • Deep Neural Network Control, PI, ONR, 2021-2025.
  • Planning Grant: Engineering Research Center for Integrative Manufacturing and Remanufacturing Technologies (iMart) to Spur Rural Development, Co-PI, NSF, 2019-2021.
  • A Doctoral Program in Big Data, Machine Learning, and Analytics for Security and Safety”, Co-PI, Dept. of Education, 2018-2021.
  • RFID based Asset Tracking and Evolvable DNA, Honeywell, PI, 2020-2021.

Selected Awards

  • 2021 University of Missouri Presidential Award for Sustained Excellence-STEM
  • 2020 Best Associate Editor Award, IEEE Systems, Man, and Cybernetics-Systems.
  • 2018 IEEE Control System Society’s Transition to Practice Award
  • 2018 Fellow, National Academy of Inventors
  • 2016 Fellow of the IEEE
  • 2015 Fellow of the IET (UK)
  • 2014 Fellow of the Inst. Of Measurement & Control (UK)
  • 2005 Teaching Commendation Award
  • Commended for Teaching Excellence in 2006-2007, 2012-2013, 2013-2014
  • Outstanding Teaching Award 2014-2015, 2015-2016, 2017-2018
  • Faculty Excellence Award 2005-2006, 2006-2007
  • 2007 Boeing Pride Achievement Award
  • 2001 University of Texas Presidential Award for Excellence (early career)
  • 2001 Caterpillar Research Excellence Award
  • 2000 NSF Career Award

Students Graduated (recent)

  • Rohollah Moghadam, “Optimal adaptive control of timed-delay dynamical systems with known and uncertain dynamics”, October 2020. (Assistant Professor, Arkansas Tech. University)

 

 

 

Research Interests:

Systems and control; Neural network control; Event triggered control/cyber-physical systems; Resilience/prognostics; Autonomous systems/robotics

Resume/CV:

Personal Website:

Education:

  • Doctor of Philosophy in Electrical Engineering (1/92-8/94) Automation and Robotics Research Institute, University of Texas at Arlington Specialization: Nonlinear Adaptive Neural Network Control
  • Master of Science (9/87-12/89); University of Saskatchewan at Saskatoon, Canada Specialization: Embedded Control Systems and Robotics
  • Bachelor of Electrical Engineering (7/82-8/86); Anna University at Madras, India