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Univariate Theory of Functional Connections Applied to Component Constraints

1
Aerospace Engineering, Texas A&M University, College Station, TX 77843, USA
2
Systems and Industrial Engineering, Aerospace and Mechanical Engineering, The University of Arizona, Tucson, AZ 85721, USA
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This paper is an extended version of our paper published in Proceedings of the 2018 AAS/AIAA Astrodynamics Specialist Conference, Snowbird, UT, USA, 19–23 August 2018.
Math. Comput. Appl. 2021, 26(1), 9; https://doi.org/10.3390/mca26010009
Received: 17 December 2020 / Revised: 10 January 2021 / Accepted: 11 January 2021 / Published: 14 January 2021
This work presents a methodology to derive analytical functionals, with embedded linear constraints among the components of a vector (e.g., coordinates) that is a function a single variable (e.g., time). This work prepares the background necessary for the indirect solution of optimal control problems via the application of the Pontryagin Maximum Principle. The methodology presented is part of the univariate Theory of Functional Connections that has been developed to solve constrained optimization problems. To increase the clarity and practical aspects of the proposed method, the work is mostly presented via examples of applications rather than via rigorous mathematical definitions and proofs. View Full-Text
Keywords: constraint optimization; functional interpolation; indirect optimal control constraint optimization; functional interpolation; indirect optimal control
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MDPI and ACS Style

Mortari, D.; Furfaro, R. Univariate Theory of Functional Connections Applied to Component Constraints. Math. Comput. Appl. 2021, 26, 9. https://doi.org/10.3390/mca26010009

AMA Style

Mortari D, Furfaro R. Univariate Theory of Functional Connections Applied to Component Constraints. Mathematical and Computational Applications. 2021; 26(1):9. https://doi.org/10.3390/mca26010009

Chicago/Turabian Style

Mortari, Daniele, and Roberto Furfaro. 2021. "Univariate Theory of Functional Connections Applied to Component Constraints" Mathematical and Computational Applications 26, no. 1: 9. https://doi.org/10.3390/mca26010009

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