A tree structure algorithm for optimal control problems with state constraints

We present a tree structure algorithm for optimal control problems with state constraints. We prove a convergence result for a discrete time approximation of the value function based on a novel formulation in the case of convex constraints. Then the Dynamic Programming approach is developed by a d...

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Main Authors: Alessandro Alla, Maurizio Falcone, Luca Saluzzi
Format: Article
Language:English
Published: Sapienza Università Editrice 2020-06-01
Series:Rendiconti di Matematica e delle Sue Applicazioni
Subjects:
Online Access:https://www1.mat.uniroma1.it/ricerca/rendiconti/ARCHIVIO/2020(3-4)/193-221.pdf
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spelling doaj-e3954a4a08a24bfc878f4e88c1afec352020-12-22T15:11:30ZengSapienza Università EditriceRendiconti di Matematica e delle Sue Applicazioni1120-71832532-33502020-06-01413-4193221A tree structure algorithm for optimal control problems with state constraintsAlessandro Alla0Maurizio Falcone 1Luca Saluzzi2PUC-RIO, Rua Marques de Sao Vicente, 225, G´avea, Rio de Janeiro, RJ, Brasil, 22451-900.Sapienza Università di Roma, Piazzale Aldo Moro, 5, 00185 Roma, Italy.Sapienza Università di Roma, Piazzale Aldo Moro, 5, 00185 Roma, Italy.We present a tree structure algorithm for optimal control problems with state constraints. We prove a convergence result for a discrete time approximation of the value function based on a novel formulation in the case of convex constraints. Then the Dynamic Programming approach is developed by a discretization in time leading to a tree structure in space derived by the controlled dynamics, taking into account the state constraints to cut several branches of the tree. Moreover, an additional pruning allows for the reduction of the tree complexity as for the case without state constraints. Since the method does not use an a priori space grid, no interpolation is needed for the reconstruction of the value function and the accuracy essentially relies on the time step h. These features permit a reduction in CPU time and in memory allocations. The synthesis of optimal feedback controls is based on the values on the tree and an interpolation on the values obtained on the tree will be necessary if a different discretization in the control space is adopted, e.g. to improve the accuracy of the method in the reconstruction of the optimal trajectories. Several examples show how this algorithm can be applied to problems in low dimension and compare it to a classical DP method on a grid. https://www1.mat.uniroma1.it/ricerca/rendiconti/ARCHIVIO/2020(3-4)/193-221.pdfoptimal controlstate constraintsdynamic programmingtree structureviscosity solutions.
collection DOAJ
language English
format Article
sources DOAJ
author Alessandro Alla
Maurizio Falcone
Luca Saluzzi
spellingShingle Alessandro Alla
Maurizio Falcone
Luca Saluzzi
A tree structure algorithm for optimal control problems with state constraints
Rendiconti di Matematica e delle Sue Applicazioni
optimal control
state constraints
dynamic programming
tree structure
viscosity solutions.
author_facet Alessandro Alla
Maurizio Falcone
Luca Saluzzi
author_sort Alessandro Alla
title A tree structure algorithm for optimal control problems with state constraints
title_short A tree structure algorithm for optimal control problems with state constraints
title_full A tree structure algorithm for optimal control problems with state constraints
title_fullStr A tree structure algorithm for optimal control problems with state constraints
title_full_unstemmed A tree structure algorithm for optimal control problems with state constraints
title_sort tree structure algorithm for optimal control problems with state constraints
publisher Sapienza Università Editrice
series Rendiconti di Matematica e delle Sue Applicazioni
issn 1120-7183
2532-3350
publishDate 2020-06-01
description We present a tree structure algorithm for optimal control problems with state constraints. We prove a convergence result for a discrete time approximation of the value function based on a novel formulation in the case of convex constraints. Then the Dynamic Programming approach is developed by a discretization in time leading to a tree structure in space derived by the controlled dynamics, taking into account the state constraints to cut several branches of the tree. Moreover, an additional pruning allows for the reduction of the tree complexity as for the case without state constraints. Since the method does not use an a priori space grid, no interpolation is needed for the reconstruction of the value function and the accuracy essentially relies on the time step h. These features permit a reduction in CPU time and in memory allocations. The synthesis of optimal feedback controls is based on the values on the tree and an interpolation on the values obtained on the tree will be necessary if a different discretization in the control space is adopted, e.g. to improve the accuracy of the method in the reconstruction of the optimal trajectories. Several examples show how this algorithm can be applied to problems in low dimension and compare it to a classical DP method on a grid.
topic optimal control
state constraints
dynamic programming
tree structure
viscosity solutions.
url https://www1.mat.uniroma1.it/ricerca/rendiconti/ARCHIVIO/2020(3-4)/193-221.pdf
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