In this paper, we develop a safe control synthesis method that integrates state estimation and parameter estimation within an adaptive optimal control (AOC) and control barrier function (CBF)-based control architecture. The developed approach decouples safety objectives from the learning objectives using a CBF-based guarding controller where the CBFs are robustified to account for the lack of full-state measurements. The coupling of this guarding controller with the AOC-based stabilizing control guarantees safety and regulation despite the lack of full state measurement. The paper leverages recent advancements in deep neural network-based adaptive observers to ensure safety in the presence of state estimation errors. Safety and convergence guarantees are provided using a Lyapunov-based analysis, and the effectiveness of the developed controller is demonstrated through simulation under mild excitation conditions.
@article{arxiv.2510.20081,
title = {Safe Output-Feedback Adaptive Optimal Control of Affine Nonlinear Systems},
author = {Tochukwu E. Ogri and Muzaffar Qureshi and Zachary I. Bell and Wanjiku A. Makumi and Rushikesh Kamalapurkar},
journal= {arXiv preprint arXiv:2510.20081},
year = {2025}
}