Optimistic planning algorithms for state-constrained optimal control problems

In this work, we study optimistic planning methods to solve some state-constrained optimal control problems in finite horizon. While classical methods for calculating the value function are generally based on a discretization in the state space, optimistic planning algorithms have the advantage of u...

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Bibliographic Details
Published in:Computers & mathematics with applications (1987) Vol. 109; no. 1; pp. 158 - 179
Main Authors: Bokanowski, Olivier, Gammoudi, Nidhal, Zidani, Hasnaa
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.03.2022
Elsevier BV
Elsevier
Subjects:
Adaptive control
Algorithms
Artificial Intelligence
Computational Complexity
Computer Science
Constraints
Discretization
Mathematics
Numerical Analysis
Optimal control
Optimal control problems
Optimistic planning algorithm
Optimization
Optimization and Control
Piecewise control policies
State constraints
Piecewise control policies
Optimal control problems
Optimistic planning algorithm
State constraints
optimal control problems
state constraints
ISSN:0898-1221, 1873-7668
Online Access:Get full text
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Summary:In this work, we study optimistic planning methods to solve some state-constrained optimal control problems in finite horizon. While classical methods for calculating the value function are generally based on a discretization in the state space, optimistic planning algorithms have the advantage of using adaptive discretization in the control space. These approaches are therefore very suitable for control problems where the dimension of the control variable is low and allow to deal with problems where the dimension of the state space can be very high. Our algorithms also have the advantage of providing, for given computing resources, the best control strategy whose performance is as close as possible to optimality while its corresponding trajectory comply with the state constraints up to a given accuracy.
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ISSN:0898-1221
1873-7668
DOI:10.1016/j.camwa.2022.01.016