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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Sapienza Università Editrice
2020-06-01
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Online Access: | https://www1.mat.uniroma1.it/ricerca/rendiconti/ARCHIVIO/2020(3-4)/193-221.pdf |
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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 |
work_keys_str_mv |
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