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 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 167 peer-reviewed journal articles, over 281 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 27 doctoral students and 30 M.S. students. His research funding is in excess of $16 million dollars from NSF, NASA, AFRL, Sandia and from other companies. His current research interests include adaptive and neural network control, networked control systems/cyber physical systems, prognostics, and autonomous systems/robotics.  He served on various editorial boards and as a co-editor for the IET Book series on Control.

Journal Papers

  • W Meng, Qinmin Yang, S. Jagannathan, and Youxian Sun, “Distributed control of high-order nonlinear input constrained multiagent systems using a backstepping-free method”, IEEE Transactions on Cybernetics, vol. 49, no. 11, pp. 3923-3933, November 2019.
  •  B. Fan, Q. Yang, S. Jagannathan, Y. Sun, “Output-constrained control for non-affine multi-agent systems with partially unknown control directions,” IEEE Transactions on Automatic Control, vol. 64, no. 9, pp. 3936-3942, September 2019.
  •  V. Narayanan, A. Sahoo, and S. Jagannathan, “A min-max approach to event and self-triggered sampling and regulation of linear systems" IEEE Transactions on Industrial Electronics, vol. 66, no. 7, pp. 5433-5440, July 2019.
  •  V. Narayanan, S. Jagannathan, K. Ramkumar, "Event-sampled output feedback control of robot manipulators using neural networks" IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 6, pp. 1651-1658, June 2019.
  •  V. Narayanan, A. Sahoo, S. Jagannathan, K. George, "Approximate optimal distributed control of nonlinear interconnected systems using event-triggered nonzero-sum games" IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 5, pp. 1512-1522, May 2019.
  • Haci Guzey, Travis Dierks, S. Jagannathan, and Levent Acar, “Modified consensus-based output feedback control of quadrotor UAV formations using neural networks", Journal of Intelligent and Robotic Systems, pp. 1-18, Online November 2018 10.1007/s10846-018-0961-y, vol. 94, no.1, pp. 283-300, April 2019.

 

Conference Papers

  • C. Bhowmick and S. Jagannathan, “Detection and mitigation of attacks in nonlinear stochastic systems using modified chi square detector”, Proc. of the IEEE Conference on Decision and Control, December 2019.
  • R. 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, December 2019.
  • Haifeng Niu, C. Bhowmick, A Sahoo and S. Jagannathan, “Attack detection in linear networked control systems by using learning methodology”, Proc. of the IEEE Conference on Controls Technology and Applications (CCTA), pp. 148-153, August 2019.
  • C. Bhowmick and S. Jagannathan, “Detection of sensor attacks for uncertain stochastic linear systems”, Proc. of the IEEE Conference on Controls Technology and Applications (CCTA), pp. 706-711, August 2019.
  • A. Raj, S. Jagannathan, T. Yucelen, “Event-triggered adaptive distributed state estimation by using active-passive sensor networks”, Proc. of American Controls Conference, pp.4695-4700, June 2019.
  • D. Petersen, T. Yucelen, and S. Jagannathan, “Active-passive dynamic consensus filters for linear time-invariant multiagent systems”, Proc. of American Controls Conference, pp. 4683-4688, June 2019.
  • A. Raj, S. Jagannathan, T. Yucelen, “Distributed state estimation by using active-passive sensor networks”, Proc. of American Controls Conference, pp. 4689-4694, June 2019.

Books Published

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

Book Chapter(s)

  • Krishnan Raghavan, S. Jagannathan, and V. Samaranayake, “Direct error driven learning for classification with applications to Bigdata”, Editors: W. Pedrycz and S. Chen, Springer 2019.
  • Haifeng Niu, C. Bhowmick, and S. Jagannathan, “Attack detection and estimation for cyber-physical systems by using learning methodology”, in Artificial Neural Networks in Engineering Applications, Editors: Alma Y. Alanis, Nancy Arana-Daniel and Carlos Lopez-Franco, Elsevier, pp. 107-126, 2019.

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.

Recent Grants (active)

  • Planning Grant: Engineering Research Center for Integrative Manufacturing and Remanufacturing Technologies (iMart) to Spur Rural Development, Co-PI, NSF, 2019-2020.
  • A Doctoral Program in Big Data, Machine Learning, and Analytics for Security and Safety”, Co-PI, 2018-2021.
  • RFID based Asset Tracking and Evolvable DNA, Honeywell, PI, 2018-2019
  • Event Triggered Control of  Networked Control Systems by using Adaptive Dynamic Programming, PI- NSF, 2014-2019.

 

Selected Awards

  • 2018 IEEE Control System Society’s Transition to Practice Award
  • Fellow, National Academy of Inventors
  • Fellow of the IEEE
  • Fellow of the IET (UK), Fellow of the Inst. Of Measurement & Control (UK)
  • Outstanding Teaching Award 2015, 2017
  • Faculty Excellence Award 2005-2006, 2006-2007

 

Students Graduated (recent)

  • Krishnan Raghavan, “Deep learning neural network based classifier design with applications to bigdata analytics”, March 2019. (Now at Argonne National Laboratory, Chicago)

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