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Treffer: Algorithm 902: GPOPS, A MATLAB Software for Solving Multiple-Phase Optimal Control Problems Using the Gauss Pseudospectral Method

Title:
Algorithm 902: GPOPS, A MATLAB Software for Solving Multiple-Phase Optimal Control Problems Using the Gauss Pseudospectral Method
Authors:
RAO, Anil V, HUNTINGTON, Geoffrey T, BENSON, David A, DARBY, Christopher, PATTERSON, Michael A, FRANCOLIN, Camila, SANDERS, Ilyssa
Source:
ACM transactions on mathematical software. 37(2)
Publisher Information:
New York, NY: Association for Computing Machinery, 2011.
Publication Year:
2011
Physical Description:
print, 2 p.1/4
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse mathématique, Mathematical analysis, Equations différentielles, Ordinary differential equations, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Génie logiciel, Software engineering, Commande optimale, Optimal control, Control óptimo, Commande phase, Phase control, Control fase, Contrôle optimal, Optimal control (mathematics), Control óptimo (matemáticas), Coût, Costs, Coste, Discrétisation, Discretization, Discretización, Développement logiciel, Software development, Desarrollo logicial, FORTRAN, Langage programmation, Programming language, Lenguaje programación, Liaison génétique, Genetic linkage, Ligamiento genético, Méthode Gauss, Gauss method, Método Gauss, Méthode spectrale, Spectral method, Método espectral, Outil logiciel, Software tool, Herramienta software, Programmation dynamique, Dynamic programming, Programación dinámica, Programmation non linéaire, Non linear programming, Programación no lineal, Système grande taille, Large scale system, Sistema gran escala, Algorithms, Dynamic optimization, Languages, computational methods, nonlinear optimization, nonlinear programming, optimal control, phases
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
University of Florida, United States
Blue Origin, LLC, United States
The Charles Stark Draper Laboratory, Inc., United States
ISSN:
0098-3500
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=24358914
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Mathematics

Operational research. Management
Accession Number:
edscal.24358914
Database:
PASCAL Archive

Weitere Informationen

An algorithm is described to solve multiple-phase optimal control problems using a recently developed numerical method called the Gauss pseudospectrul method. The algorithm is well suited for use in modern vectorized programming languages such as FORTRAN 95 and MATLAB. The algorithm discretizes the cost functional and the differential-algebraic equations in each phase of the optimal control problem. The phases are then connected using linkage conditions on the state and time. A large-scale nonlinear programming problem (NLP) arises from the discretization and the significant features of the NLP are described in detail. A particular reusable MATLAB implementation of the algorithm, called GPOPS, is applied to three classical optimal control problems to demonstrate its utility. The algorithm described in this article will provide researchers and engineers a useful software tool and a reference when it is desired to implement the Gauss pseudospectral method in other programming languages.