@article{LI2026, 
author = {Yu LI and Xinlong XU and Kecheng LI and Chi-yung WEN and Ni LI and Xiaoxiong LIU},
title = {Deep-stall recovery control based on safety-constrained reinforcement learning},
year = {2026},
journal = {Acta Aeronautica et Astronautica Sinica},
volume = {47},
number = {4},
keywords = {V-tail aircraft, deep-stall recovery, penalized proximal policy optimization, fast predefined-time control, state safety constraints},
url = {https://www.sciopen.com/article/10.7527/S1000-6893.2025.32217},
doi = {10.7527/S1000-6893.2025.32217},
abstract = {To address the deep-stall recovery problem of V-tail aircraft, this paper proposes a novel deep-stall recovery hierarchical strategy that combines Penalized Proximal Policy Optimization (P3O) reinforcement learning with a fast predefined-time incremental control approach. First, a six-degree-of-freedom nonlinear model of the V-tail aircraft is established, and the deep-stall recovery problem is formulated as a constrained Markov decision process. Second, the existing predefined-time control theory is improved to enhance the transient performance of state responses under given convergence time. Based on this improved theory and a nonlinear incremental dynamic inversion method, an angular rate controller is designed, which ensures that angular rate accurately tracks the decision commands within the user-defined time. The predefined-time stability of the controller is theoretically proven via Lyapunov stability theory. Subsequently, a decision-making network based on P3O is constructed to improve the safety during deep-stall recovery, where safety constraints are incorporated as penalty terms to guide the agent in generating safe recovery actions. Finally, a series of simulations and Monte Carlo experiments are conducted to validate the proposed strategy. The results demonstrate its superior performance in terms of rapidity, robustness, safety, and interpretability.}
}