Analysis, Correlation, and Estimation for Control of Material Properties
Year: 2018
Type: article
Abstract: System identification algorithms use data to obtain mathematical models of systems that fits the data, permitting the model to be used to predict and design controls for system behavior beyond the scope of the data. Thus, accurate modeling and characterization of the system equations are very important features of any mission. These system equations are principally populated with variables from physics (e.g. material properties). Simple control algorithms begin by using the governing physics expressed in mathematical models for control, but usually more advanced techniques are required to mitigate noise, mismodeled system parameters, of unknown/un-modeled effects, in addition to disturbances. Characterization of the physical system a priori is an important step, and this research article will describe research into identifying system equations for several complex structures. Under the auspices of the Digital Manufacturing Analysis, Correlation and Estimation (DMACE) (pronounced “DEE-MACE”) Challenge, the Defense Advanced Research Projects Agency (DARPA) digitally manufactured several complex structures and then conduct a series of structural load tests upon them to determine material properties. Data from the manufacture and load tests was then posted on the worldwide web. Participants were challenged to develop a correlation model that accurately correlates digital manufacturing (DM) machine inputs to output structural test data. Participant models were then evaluated by their ability to predict the test results of the final DM structures. The model that most accurately predicted the final test results won the Challenge. Many disparate technical approaches were investigated by researchers from all over the world, and this paper introduces readers to several of those interesting technical approaches. The authors have permission to publish these government owned submissions, but every efforts is made to credit the researchers themselves, and furthermore each submission is presented in its original form to the maximum extent permitted by the journal’s peer reviewers and editors.
Authors: Timothy Sands, Clinton Armani
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2787371019