Psychological constructs in foreign policy prediction

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67914/2/10.1177_002200276701100304.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

Adaptive Control of a Seeker-Guided 2D Missile with Unmodeled Aerodynamics

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97113/1/AIAA2012-4617.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

The ASPIC Project: A virtual assistant for HMI Coaching

To carry out their missions successfully, pilots must perform a certain number of actions to configure their user interface and carry out complex tasks (monitoring of the tactical situation, sensors, communication, tracks identification …). This can be tedious and critical during high workload phases of the mission as an important number of actions are required in short time spans. In this study, we intend to optimize the pilots UX by creating a virtual assistant able to recommend the best interface configuration based on the pilot’s actions and current mission contexts. The ASPIC project aims at evaluating the feasibility of HMI interaction recommandation. In order to evaluate this latter while generating relevant data to train the assistant, we worked on a flight mission simulator

Interference dynamics in mixed red alder/Douglas-fir forests

This study characterized the nature and dynamics of interference in mixed red alder (Alnus rubra Bong.)/Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco) stands in the Pacific Northwest, USA. Long-term spatial and tree measurements from the Cascade Head (CH) and H.J. Andrews (HJA) Experimental Forests in western Oregon and Delezene Creek (DC), Washington were utilized to investigate neighborhood and population-level measures of interference. Existing neighborhood and population-level measures of interference were modified to evaluate the intensity and importance of intra- and inter-specific interference. The relationship between relative growth rate and population-level and neighborhood interference were examined over 9 years at the CH and HJA study sites and 38 years at the DC study site. In general, the effects of intra-specific interacted with the effects of inter-specific interference to influence the relative growth rates of red alder and Douglas-fir at all of the sites. Performance of the interference measures as predictors of relative growth rates varied between species and with stand structure. In general, population-level indices were the best predictors of relative growth rates for the species with heights greater than the other interacting species over a given interval of time. In contrast, neighborhood indices were the best predictors of relative growth rates for the species with subordinate or equivalent tree heights to the dominant species over a given interval of time. These results were consistent for both species, all three study sites, and all measurement periods when interference occurred and suggest that the importance of neighborhood interference varies with the competitive status of a species. A conceptual model synthesizes the importance of neighborhood and population-level interference as a function of relative dominance of a species. In addition, the literature suggests that this model may also be appropriate for individuals within a population

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

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

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

Structural Health Determination and Model Refinement for a Deployable Composite Boom

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77171/1/AIAA-2009-2373-948.pd