Physical Parameter Estimation with Ensemble Learning for State Estimation of High-Rate Dynamic Systems

Vahid Barzegar; Matthew Nelson; Simon Laflamme; Chao Hu; Jacob Dodson

doi:10.1117/12.2613113

18 April 2022 Physical Parameter Estimation with Ensemble Learning for State Estimation of High-Rate Dynamic Systems

Vahid Barzegar, Matthew Nelson, Simon Laflamme, Chao Hu, Jacob Dodson

Proceedings Volume PC12046, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022; PC1204607 (2022) https://doi.org/10.1117/12.2613113
Event: SPIE Smart Structures + Nondestructive Evaluation, 2022, Long Beach, California, United States

Abstract

High-rate dynamic systems are defined as systems that undergo large levels of acceleration, often over 100g, over short durations, typically less than 100 ms. Examples of such systems include active blast mitigation mechanisms, adaptive air bag deployment, and hypersonic systems. Their dynamics is uniquely characterized by 1) large uncertainties in the external loads; 2) high levels of nonstationarities and heavy disturbances; and 3) unmodeled dynamics generated from changes in system configurations. High-rate structural health monitoring (HRSHM) is concerned with the development of sub-millisecond state estimation capabilities in order to facilitate the future implementation of decision systems to improve the safety and operation of high-rate systems.

Conference Presentation

Citation Download Citation

Vahid Barzegar, Matthew Nelson, Simon Laflamme, Chao Hu, and Jacob Dodson "Physical Parameter Estimation with Ensemble Learning for State Estimation of High-Rate Dynamic Systems", Proc. SPIE PC12046, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022, PC1204607 (18 April 2022); https://doi.org/10.1117/12.2613113

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