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

Retrospective Cost Adaptive Control for Systems with Unknown Nonminimum-Phase Zeros

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90721/1/AIAA-2011-6203-626.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

Comments on ‘output feedback adaptive command following and disturbance rejection for nonminimum phase uncertain dynamical systems’

We provide numerical examples and analysis to show that the adaptive controller given by Theorem 3.1 of Yucelen et al. 1 may fail to stabilize plants under the stated conditions. Copyright © 2011 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83465/1/1235_ftp.pd

Damage Localization for Structural Health Monitoring Using Retrospective Cost Model Refinement

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83574/1/AIAA-2010-2628-530.pd

Investigation of Cumulative Retrospective Cost Adaptive Control for Missile Application

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83634/1/AIAA-2010-7577-578.pd

Adaptive State Estimation for Nonminimum-Phase Systems with Uncertain Harmonic Inputs

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90727/1/AIAA-2011-6315-484.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

Threats to North American Forests from Southern Pine Beetle with Warming Winters

In coming decades, warmer winters are likely to lift range constraints on many cold-limited forest insects. Recent unprecedented expansion of the southern pine beetle (SPB, Dendroctonus frontalis) into New Jersey, New York, Connecticut, and Massachusetts in concert with warming annual temperature minima highlights the risk that this insect pest poses to the pine forests of the northern United States and Canada under continued climate change. Here we present the first projections of northward expansion in SPB-suitable climates using a statistical bioclimatic range modeling approach and current-generation general circulation model (GCM) output under the RCP 4.5 and 8.5 emissions scenarios. Our results show that by the middle of the 21st century, the climate is likely to be suitable for SPB expansion into vast areas of previously unaffected forests throughout the northeastern United States and into southeastern Canada. This scenario would pose a significant economic and ecological risk to the affected regions, including disruption oflocal ecosystem services, dramatic shifts in forest structure, and threats to native biodiversity