CPS: Medium: Collaborative Research: Robust Capacity-Constrained Scheduling and Data-Based Model Refinement for Enhanced Collision Avoidance in Low-Earth Orbit
Lead PI:
Dennis Bernstein
The objective of this research is to improve the ability to track the orbits of space debris and thereby reduce the frequency of collisions. The approach is based on two scientific advances: 1) optimizing the scheduling of data transmission from a future constellation of orbiting Cubesats to ground stations located worldwide, and 2) using satellite data to improve models of the ionosphere and thermosphere, which in turn are used to improve estimates of atmospheric density. Intellectual Merit Robust capacity-constrained scheduling depends on fundamental research on optimization algorithms for nonlinear problems involving both discrete and continuous variables. This objective depends on advances in optimization theory and computational techniques. Model refinement depends on adaptive control algorithms, and can lead to fundamental advances for automatic control systems. These contributions provide new ideas and techniques that are broadly applicable to diverse areas of science and engineering. Broader Impacts Improving the ability to predict the trajectories of space debris can render the space environment safer in both the near term---by enhancing astronaut safety and satellite reliability---and the long term---by suppressing cascading collisions that could have a devastating impact on the usage of space. This project will impact real-world practice by developing techniques that are applicable to large-scale modeling and data collection, from weather prediction to Homeland Security. The research results will impact education through graduate and undergraduate research as well as through interdisciplinary modules developed for courses in space science, satellite engineering, optimization, and data-based modeling taught across multiple disciplines.
Dennis Bernstein


Professor Bernstein's interest include identification, estimation, and control for aerospace applications. His research has focused on active noise and vibration control, as well as attitude control for space applications. His current interests are in the theory and application of nonlinear system identification, large-scale state estimation for data assimilation, and adaptive control. He is directorof the Noise, Vibration, and Motion Control Laboratory, which includes instrumentation and testbeds for control applications. A 6-degree-of-freedom electric shaker table under all-digital control is used for vibration and motion control applications. Facilities are available for implementing and testing algorithms for active noise and vibration control. Current research is focusing on adaptive command following and disturbance rejection algorithms for systems with uncertain dynamics and unknown disturbance spectra. He is co-director (with Ilya Kolmanovsky) of the Attitude Dynamics and Control Laboratory. In this laboratory, a triaxial air bearing is used to develop and implement adaptive control algorithms for spacecraft applications. He was Editor-in-Chief of the IEEE Control Systems Magazine from 2003 to 2011.

ProfBernstein has authored more than 200 journal papers and 350 conference papers. He is the author of Matrix Mathematics, which is published by Princeton University Press. (A review of Matrix Mathematics can be downloaded from:http://www.siam.org/news/news.php?id=125) Matrix Mathematics - Errata and Addenda

Performance Period: 09/15/2010 - 08/31/2014
Institution: University of Michigan Ann Arbor
Sponsor: National Science Foundation
Award Number: 1035236