Artículo
Geometric Integrators for Higher-Order Variational Systems and Their Application to Optimal Control
Fecha de publicación:
01/12/2016
Editorial:
Springer
Revista:
Journal Of Nonlinear Science
ISSN:
0938-8974
e-ISSN:
1432-1467
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Numerical methods that preserve geometric invariants of the system, such as energy, momentum or the symplectic form, are called geometric integrators. In this paper we present a method to construct symplectic-momentum integrators for higher-order Lagrangian systems. Given a regular higher-order Lagrangian L: T( k )Q→ R with k≥ 1 , the resulting discrete equations define a generally implicit numerical integrator algorithm on T( k - 1 )Q× T( k - 1 )Q that approximates the flow of the higher-order Euler–Lagrange equations for L. The algorithm equations are called higher-order discrete Euler–Lagrange equations and constitute a variational integrator for higher-order mechanical systems. The general idea for those variational integrators is to directly discretize Hamilton’s principle rather than the equations of motion in a way that preserves the invariants of the original system, notably the symplectic form and, via a discrete version of Noether’s theorem, the momentum map. We construct an exact discrete Lagrangian Lde using the locally unique solution of the higher-order Euler–Lagrange equations for L with boundary conditions. By taking the discrete Lagrangian as an approximation of Lde, we obtain variational integrators for higher-order mechanical systems. We apply our techniques to optimal control problems since, given a cost function, the optimal control problem is understood as a second-order variational problem.
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Articulos(CCT - BAHIA BLANCA)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - BAHIA BLANCA
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - BAHIA BLANCA
Citación
Colombo, Leonardo Jesus; Ferraro, Sebastián José; Martin de Diego, David; Geometric Integrators for Higher-Order Variational Systems and Their Application to Optimal Control; Springer; Journal Of Nonlinear Science; 26; 6; 1-12-2016; 1615-1650
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