We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Data‐driven policy iteration algorithm for continuous‐time stochastic linear‐quadratic optimal control problems.
- Authors
Zhang, Heng; Li, Na
- Abstract
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.
- Subjects
STOCHASTIC control theory; OPTIMAL control theory; CONTINUOUS time models; KRONECKER products; RICCATI equation; STOCHASTIC systems; ALGEBRAIC equations
- Publication
Asian Journal of Control, 2024, Vol 26, Issue 1, p481
- ISSN
1561-8625
- Publication type
Article
- DOI
10.1002/asjc.3223