2025/11/27 by Dongdong Li, Li, Dongdong, Jiuxiang Dong +1
Computer Science · Engineering · #Adaptive Dynamic Programming Control #Advanced Control Systems Optimization #FOS: Electrical engineering #Stability and Control of Uncertain Systems #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2511.22160
openalex publication_date 2025/11/27 · openalex created_date 2025/12/03 · openalex updated_date 2026/07/28
This note proposes a data-driven output-feedback stabilizing policy iteration for unknown linear discrete-time systems with unmeasurable states. Existing policy iteration methods for optimal control must start from a stabilizing control policy, which is particularly challenging to obtain for unknown systems, especially when states are unavailable. In such cases, it is more difficult to guarantee stability and convergence performance. To address this problem, an output-feedback stabilizing policy iteration framework is developed to learn closed-loop stabilizing control policies while ensuring convergence performance. Specifically, cumulative scalar parameters are introduced to compress the original system to a stable scale. Then, by integrating modified policy iteration with parameter update rules, the system is gradually amplified/restored to the original system while preserving stability such that the stabilizing control policy is obtained. The entire process is driven solely by input-output data. Moreover, a stability analysis is provided for output-feedback. The proposed approach is validated by simulations.