Data‐driven policy iteration algorithm for continuous‐time stochastic linear‐quadratic optimal control problems

This paper studies a continuous‐time stochastic linear‐quadratic (SLQ) optimal control problem on infinite‐horizon. Combining the Kronecker product theory with an existing policy iteration algorithm, a data‐driven policy iteration algorithm is proposed to solve the problem. In contrast to most exist...

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Veröffentlicht in:Asian journal of control Jg. 26; H. 1; S. 481 - 489
Hauptverfasser: Zhang, Heng, Li, Na
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken Wiley Subscription Services, Inc 01.01.2024
Schlagworte:
Data collection
data‐driven
Iterative algorithms
Optimal control
policy iteration
Riccati equation
stochastic algebraic Riccati equation
stochastic linear‐quadratic optimal control problem
Stochastic systems
ISSN:1561-8625, 1934-6093
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Zusammenfassung:This paper studies a continuous‐time stochastic linear‐quadratic (SLQ) optimal control problem on infinite‐horizon. Combining the Kronecker product theory with an existing policy iteration algorithm, a data‐driven policy iteration algorithm is proposed to solve the problem. In contrast to most existing methods that need all information of system coefficients, the proposed algorithm eliminates the requirement of three system matrices by utilizing data of a stochastic system. More specifically, this algorithm uses the collected data to iteratively approximate the optimal control and a solution of the stochastic algebraic Riccati equation (SARE) corresponding to the SLQ optimal control problem. The convergence analysis of the obtained algorithm is given rigorously, and a simulation example is provided to illustrate the effectiveness and applicability of the algorithm.
Bibliographie:ObjectType-Article-1
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ISSN:1561-8625
1934-6093
DOI:10.1002/asjc.3223