Data-Driven Modeling of High-Q Cavity Fields Using Dynamic Mode Decomposition

Nayak, Indranil; Teixeira, Fernando L.

doi:10.1109/ap-s/usnc-ursi47032.2022.9886176

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…DMD is an effective technique for learning low-order dynamics of complex systems and predicting their future behavior Schmid (2010); Schmid et al (2011);Tu et al (2013). It is based on Koopman operator theory Koopman and Neumann (1932); Koopman (1931) and has gained in popularity over the past two decades owing to its efficiency in extracting underlying characteristic features of the dynamic system Nayak et al (2021b); Han and Tan (2020); Broatch et al (2019); Kutz et al (2016); Nayak and Teixeira (2022). It compares favorably in this regard to other model-order reduction methods such as proper orthogonal decomposition (POD), bi-orthogonal decomposition (BOD) and principal component analysis (PCA).…”

Section: Introductionmentioning

confidence: 99%

Hybrid A* path search with resource constraints and dynamic obstacles

Cortez

Ford

Nayak

et al. 2023

Front. Aerosp. Eng.

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This paper considers path planning with resource constraints and dynamic obstacles for an unmanned aerial vehicle (UAV), modeled as a Dubins agent. Incorporating these complex constraints at the guidance stage expands the scope of operations of UAVs in challenging environments containing path-dependent integral constraints and time-varying obstacles. Path-dependent integral constraints, also known as resource constraints, can occur when the UAV is subject to a hazardous environment that exposes it to cumulative damage over its traversed path. The noise penalty function was selected as the resource constraint for this study, which was modeled as a path integral that exerts a path-dependent load on the UAV, stipulated to not exceed an upper bound. Weather phenomena such as storms, turbulence and ice are modeled as dynamic obstacles. In this paper, ice data from the Aviation Weather Service is employed to create training data sets for learning the dynamics of ice phenomena. Dynamic mode decomposition (DMD) is used to learn and forecast the evolution of ice conditions at flight level. This approach is presented as a computationally scalable means of propagating obstacle dynamics. The reduced order DMD representation of time-varying ice obstacles is integrated with a recently developed backtracking hybrid A∗ graph search algorithm. The backtracking mechanism allows us to determine a feasible path in a computationally scalable manner in the presence of resource constraints. Illustrative numerical results are presented to demonstrate the effectiveness of the proposed path-planning method.

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