PhD Defense by Matthew Gross

Event Details
  • Date/Time:
    • Monday July 31, 2017
      1:00 pm - 3:00 pm
  • Location: Montgomery-Knight Room 317
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Summaries

Summary Sentence: SMART PROJECTILE PARAMETER ESTIMATION USING META-OPTIMIZATION

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Ph.D. Thesis Defense by

Matthew Gross

Advisor: Dr. Mark Costello

 

SMART PROJECTILE PARAMETER ESTIMATION USING META-OPTIMIZATION

 

1 PM Monday, July 31, 2017

Montgomery-Knight Room 317

 

Abstract

            System identification and parameter estimation are valuable tools in the analysis and design of smart projectile systems.  Given the complexity of these systems, it is convenient to work with mathematical models in place of the actual system.  Parameter estimation uses time history data of the system to determine a model that accurately matches the data.  Many techniques have been developed to perform parameter estimation, including regression methods, maximum likelihood estimators, and Kalman filters. 

            Maximum likelihood methods, in particular the output error method (OEM), pose the estimation problem in terms of an optimization problem.  OEM has seen extensive use on projectile systems, utilizing a numerical optimizer such as a Newton style algorithm to solve for unknown parameters.  These algorithms are prone to converging on local minima present in the projectile dynamics, requiring reasonable initial guesses of the parameters to ensure convergence.  However, for new smart projectile systems, prior estimates of the control parameters may not be available.  Thus, there is a need for reliable and robust parameter estimation methods that are not dependent a priori knowledge of the parameters.

            This thesis proposes a new method for smart projectile parameter estimation based on OEM.  To achieve robust and reliable parameter estimates, a new underlying optimization algorithm is formed dubbed meta-optimization.  Meta-optimization employs a diverse set of individual optimization algorithms with both local and global search capabilities.  The meta-optimizer operates by iteratively selecting a single algorithm to deploy in a stochastic manner, giving preference to algorithms which have performed well on the problem.  This approach allows synergies to develop between the individual optimizers, boosting performance beyond what each optimizer is capable of individually.  A suite of benchmark functions are used to analyze the meta-optimization framework and compare it to other existing algorithms.

            The new parameter estimation method is applied to an example smart projectile system equipped with a new control mechanism.  Both synthetic and experimental trajectory data is used to evaluate the effective of the proposed method.  For the standard projectile and a smart projectile executing a maneuver, the method obtains good estimates of the parameters for this system in the presence of measurement noise.

  

 

Committee Members

 

Dr. Mark Costello, AE (Advisor)

Dr. Brian German, AE

Dr. Eric Johnson, AE

Dr. Graeme Kennedy, AE

Dr. Aldo Ferri, ME

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In Campus Calendar
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Graduate Studies

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Faculty/Staff, Public, Undergraduate students
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Phd Defense
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  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Jul 18, 2017 - 9:25am
  • Last Updated: Jul 18, 2017 - 9:25am