Retrospective‐cost‐based 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 large‐scale models based on first‐principles physics, such as the global ionosphere–thermosphere 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: 446–458, 2011Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86874/1/10127_ftp.pd

Retrospective-Cost-Based Adaptive Input and State Estimation for the Ionosphere–Thermosphere

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140666/1/1.I010286.pd

Retrospective-Cost Subsystem Identification for the Global Ionosphere-Thermosphere Model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97111/1/AIAA2012-4602.pd

Data-Based Model Refinement Using Retrospective Cost Optimization

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83642/1/AIAA-2010-7889-194.pd

Retrospective Cost Optimization for Adaptive State Estimation, Input Estimation, and Model Refinement

AbstractRetrospective cost optimization was originally developed for adaptive control. In this paper, we show how this technique is applicable to three distinct but related problems, namely, state estimation, input estimation, and model refinement. To illustrate these techniques, we give two examples. In the first example, retrospective cost model refinement is used with synthetic data to estimate the cooling dynamics that are missing from a model of the ionosphere-thermosphere. In the second example, retrospective cost adaptive state estimation is used with data from a satellite to estimate a solar driver in the ionosphere- thermosphere, with performance gauged by using data from a second satellite

Retrospective-Cost-Based Adaptive State Estimation and Input Reconstruction for the Global Ionosphere-Thermosphere Model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97110/1/AIAA2012-4601.pd

Adaptive Input Reconstruction with Application to Model Refinement, State Estimation, and Adaptive Control.

Input reconstruction is the process of using the output of a system to estimate its input. In some cases, input reconstruction can be accomplished by determining the output of the inverse of a model of the system whose input is the output of the original system. Inversion, however, requires an exact and fully known analytical model, and is limited by instabilities arising from nonminimum-phase zeros. The main contribution of this work is a novel technique for input reconstruction that does not require model inversion. This technique is based on a retrospective cost, which requires a limited number of Markov parameters. Retrospective cost input reconstruction (RCIR) does not require knowledge of nonminimum-phase zero locations or an analytical model of the system. RCIR provides a technique that can be used for model refinement, state estimation, and adaptive control. In the model refinement application, data are used to refine or improve a model of a system. It is assumed that the difference between the model output and the data is due to an unmodeled subsystem whose interconnection with the modeled system is inaccessible, that is, the interconnection signals cannot be measured and thus standard system identification techniques cannot be used. Using input reconstruction, these inaccessible signals can be estimated, and the inaccessible subsystem can be fitted. We demonstrate input reconstruction in a model refinement framework by identifying unknown physics in a space weather model and by estimating an unknown film growth in a lithium ion battery. The same technique can be used to obtain estimates of states that cannot be directly measured. Adaptive control can be formulated as a model-refinement problem, where the unknown subsystem is the idealized controller that minimizes a measured performance variable. Minimal modeling input reconstruction for adaptive control is useful for applications where modeling information may be difficult to obtain. We demonstrate adaptive control of a seeker-guided missile with unknown aerodynamics.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91520/1/amdamato_1.pd

On the Accuracy of State Estimators for Constant and Time-Varying Parameter Estimation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106504/1/AIAA2013-5192.pd