3 research outputs found
Disturbance Rejection Using Self-Tuning ARMARKOV Adaptive Control with Simultaneous Identification
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/57807/1/HarshadSimultIDTCST2001.pd
Retrospectiveâcostâbased adaptive model refinement for the ionosphere and thermosphere
Mathematical models of physical phenomena are of critical importance in virtually all applications of science and technology. This paper addresses the problem of how to use data to improve the fidelity of a given model. We approach this problem using retrospective cost optimization, which uses data to recursively update an unknown subsystem interconnected to a known system. Applications of this technique are relevant to applications that depend on largeâscale models based on firstâprinciples physics, such as the global ionosphereâthermosphere model (GITM). Using GITM as the truth model, we demonstrate that measurements can be used to identify unknown physics. Specifically, we estimate static thermal conductivity parameters, as well as a dynamic cooling process. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 446â458, 2011Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86874/1/10127_ftp.pd
Adaptive Output Feedback Control of the NASA GTM Model with Unknown Nonminimum-Phase Zeros
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90722/1/AIAA-2011-6204-387.pd