A New Framework for Integrated Design of Sparse Sensing Networks with Guaranteed Performance

All dates for this event occur in the past.

Scott Lab E141
201 W. 19th Avenue Columbus, OH 43210
Columbus, OH 43210
United States

Speaker: Raktim Bhattacharya Professor Aerospace Engineering Texas A&M University

Bio: Raktim Bhattacharya received his B.Tech degree from the Indian Institute of Technology in Aerospace Engineering, followed by an M.S. and Ph.D. degree in Aerospace Engineering from the University of Minnesota. After that, he was a post-doctoral scholar at Caltech in the Department of Control and Dynamical Systems. After Caltech, he joined United Technologies Research Center as a research scientist. Following that, he joined the Aerospace Engineering department of Texas A&M University in 2005 and is currently a full professor. His research interests include robust control and estimation, nonlinear dynamics, robust control, uncertainty quantification, and convex optimization.

Abstract: Sensor precision and location play a critical role in the performance of modern engineering systems. Traditionally, the accuracy and location of these sensors are decided agnostic of required system-level performance, and the system designer tries to achieve the best performance within this predetermined system architecture. However, the performance limits inherent in the adhoc system architecture may prevent the designer from attaining the required performance. Even if architecture changes were allowed, it is unclear which sensors to change or where to add a new sensor and with what accuracy. It is possible that some sensors may be unnecessary or have more than needed precisions resulting in unnecessary higher costs. This problem is particularly challenging for large-scale distributed systems. This talk presents a new codesign framework for simultaneously determining sparse sensing architecture with optimal sensor precisions and the estimator that guarantees a given estimation accuracy. We will present various estimation algorithms, including Kalman Filtering, Ensemble and Unscented Kalman Filtering, and H2/Hinf optimal estimation. Multiple examples from flight control, space-situational awareness, and battery thermal estimation will be used to highlight the engineering value of this new theoretical framework

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