Univariate Theory of Functional Connections Applied to Component Constraints

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 problem...

Full description

Bibliographic Details
Main Authors: Daniele Mortari, Roberto Furfaro
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Mathematical and Computational Applications
Subjects:
Online Access:https://www.mdpi.com/2297-8747/26/1/9
id doaj-f3e8b81b791145b4bc1947a2094215cd
record_format Article
spelling doaj-f3e8b81b791145b4bc1947a2094215cd2021-01-15T00:01:44ZengMDPI AGMathematical and Computational Applications1300-686X2297-87472021-01-01269910.3390/mca26010009Univariate Theory of Functional Connections Applied to Component ConstraintsDaniele Mortari0Roberto Furfaro1Aerospace Engineering, Texas A&M University, College Station, TX 77843, USASystems and Industrial Engineering, Aerospace and Mechanical Engineering, The University of Arizona, Tucson, AZ 85721, USAThis 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.https://www.mdpi.com/2297-8747/26/1/9constraint optimizationfunctional interpolationindirect optimal control
collection DOAJ
language English
format Article
sources DOAJ
author Daniele Mortari
Roberto Furfaro
spellingShingle Daniele Mortari
Roberto Furfaro
Univariate Theory of Functional Connections Applied to Component Constraints
Mathematical and Computational Applications
constraint optimization
functional interpolation
indirect optimal control
author_facet Daniele Mortari
Roberto Furfaro
author_sort Daniele Mortari
title Univariate Theory of Functional Connections Applied to Component Constraints
title_short Univariate Theory of Functional Connections Applied to Component Constraints
title_full Univariate Theory of Functional Connections Applied to Component Constraints
title_fullStr Univariate Theory of Functional Connections Applied to Component Constraints
title_full_unstemmed Univariate Theory of Functional Connections Applied to Component Constraints
title_sort univariate theory of functional connections applied to component constraints
publisher MDPI AG
series Mathematical and Computational Applications
issn 1300-686X
2297-8747
publishDate 2021-01-01
description 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.
topic constraint optimization
functional interpolation
indirect optimal control
url https://www.mdpi.com/2297-8747/26/1/9
work_keys_str_mv AT danielemortari univariatetheoryoffunctionalconnectionsappliedtocomponentconstraints
AT robertofurfaro univariatetheoryoffunctionalconnectionsappliedtocomponentconstraints
_version_ 1724337889405304832