Welcome!
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The Information & Smart Systems Laboratory (ISSL) is a group of researchers at the University of Kansas working to push the frontiers of information-driven smart systems. The lab performs cutting-edge research on information extraction, analysis and exploitation for dynamic systems to deal with system complexity and enable system intelligence. People of ISSL are committed to pursuing new ideas, perspectives and methods synergistically combining estimation, control, optimization, network systems and machine learning to address fundamental and applied problems arising from diverse practical systems. The past and ongoing work involves optimal estimation, distributed control, advanced battery management, robotics and environmental monitoring. The lab values research in the interest of general society, actively running research-integrated education and outreach events and forging collaboration with local communities and industry partners.
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NewsJanuary 24, 2019: The paper first-authored by Ning Tian, “Nonlinear double-capacitor model for rechargeable batteries: Modeling, identification and validation”, will be forthcoming in IEEE Transactions on Control Systems Technology. Congratulations, Ning!
January 21, 2019: Babak Badnava and Hao Tu joined our lab as PhD student researchers. Welcome, Babak and Hao!
January 3, 2020: Huazhen visited Shenzhen University and gave a seminar!
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Recent PublicationsNonlinear double-capacitor model for rechargeable batteries: Modeling, identification and validation
N. Tian, H. Fang, J. Chen and Y. Wang IEEE Transactions on Control Systems Technology, 2020 Robust extended Kalman filtering for systems with measurement outliers
H. Fang, M. A. Haile and Y. Wang A new encounter between leader-follower tracking and observer-based control: Towards enhancing robustness against disturbances
C. Yan and H. Fang Systems & Control Letters, 2019 Distributed energy resources coordination over time-varying directed communication networks
T. Yang, D. Wu, H. Fang, W. Ren, H. Wang, Y. Hong, K. H. Johansson IEEE Transactions on Control of Network Systems, 2019 Observer-based distributed leader-follower tracking control: A new perspective and results
C. Yan and H. Fang International Journal of Control, 2019 3-D temperature field reconstruction for a lithium-ion battery pack: A distributed Kalman filtering approach
N. Tian, H. Fang and Y. Wang IEEE Transactions on Control Systems Technology, 2019 Energy-aware leader-follower tracking control for electric-powered multi-agent systems
C. Yan, H. Fang and H. Chao Control Engineering Practice, 2018 Optimal multi-objective charging for lithium-ion battery packs: A hierarchical control approach
Q. Ouyang, J. Chen, J. Zheng and H. Fang IEEE Transactions on Industrial Informatics, 2018 Nonlinear Bayesian estimation: From Kalman filtering to a broader horizon
H. Fang, N. Tian, Y. Wang, M. Zhou and M. A. Haile IEEE/CAA Journal of Automatica Sinica, 2018 Optimal pulse-modulated lithium-ion battery charging: Algorithms and simulation
H. Fang, C. Depcik and V. Lvovich Journal of Energy Storage, 2018 Optimal cell-to-cell balancing topology design for serially connected lithium-ion battery packs
Q. Ouyang, J. Chen, J. Zheng and H. Fang IEEE Transactions on Sustainable Energy, 2018 Revisiting the state-of-charge estimation for lithium-ion batteries - A methodical investigation of the EKF approach
Y. Wang, H. Fang, L. Zhou and T. Wada IEEE Control Systems, 2017 (cover article) |
