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

2022/09/29 by Heng Zhang, Na Li, Zhang, Heng +1 · 1 citation
Economics, Econometrics and Finance · Engineering · #Advanced Control Systems Optimization #Energy Load and Power Forecasting #FOS: Mathematics #Optimization and Control (math.OC) #Stochastic processes and financial applications

paper · pdf · doi:10.48550/arxiv.2209.14490

openalex publication_date 2022/09/29 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28

Abstract

This paper studies a continuous-time stochastic linear-quadratic (SLQ) optimal control problem on infinite-horizon. A data-driven policy iteration algorithm is proposed to solve the SLQ problem. Without knowing three system coefficient matrices, this algorithm uses the collected data to iteratively approximate a solution of the corresponding stochastic algebraic Riccati equation (SARE). A simulation example is provided to illustrate the effectiveness and applicability of the algorithm.

Cited by

• Computing stabilizing feedback gains for stochastic linear systems via policy iteration method

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