An adaptive dynamic programming-based algorithm for infinite-horizon linear quadratic stochastic optimal control problems

This paper develops a novel adaptive dynamic programming (ADP)-based model-free policy iteration (PI) algorithm to solve an infinite-horizon continuous-time linear quadratic stochastic (LQS) optimal control problem, where the diffusion term in system dynamics contains both control and state variable...

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Vydáno v:Journal of applied mathematics & computing Ročník 69; číslo 3; s. 2741 - 2760
Hlavní autor: Zhang, Heng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2023
Springer Nature B.V
Témata:
Adaptive algorithms
Algorithms
Approximation
Brownian motion
Computational Mathematics and Numerical Analysis
Dynamic programming
Euclidean space
Mathematical and Computational Engineering
Mathematics
Mathematics and Statistics
Mathematics of Computing
Optimal control
Original Research
System dynamics
Theory of Computation
93E20
Policy iteration
Model-free
Adaptive dynamic programming
93E03
Linear quadratic stochastic optimal control
ISSN:1598-5865, 1865-2085
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Shrnutí:This paper develops a novel adaptive dynamic programming (ADP)-based model-free policy iteration (PI) algorithm to solve an infinite-horizon continuous-time linear quadratic stochastic (LQS) optimal control problem, where the diffusion term in system dynamics contains both control and state variables. First, we apply Ito’s lemma and take expectations to describe a relationship among the state trajectory, the control input and the matrices to be solved. Then, without needing the information of all system coefficient matrices, the ADP-based model-free algorithm is developed to approximate the optimal control from the collected data. Moreover, we give the convergence analysis under some mild conditions. Finally, a numerical example and an illustrative application are served to show that the proposed algorithm is effective.
Bibliografie:ObjectType-Article-1
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ISSN:1598-5865
1865-2085
DOI:10.1007/s12190-023-01857-9