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

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, data‐driven algorithm is proposed to solve problem. In contrast most methods that need all information of system coefficients, eliminates requirement three matrices by utilizing data system. More specifically, this uses collected iteratively approximate and solution algebraic Riccati equation (SARE) corresponding SLQ The convergence analysis obtained given rigorously, simulation example provided illustrate effectiveness applicability algorithm.