Safe Output-Feedback Adaptive Optimal Control of Affine Nonlinear Systems

2025/10/22 by Ogri, Tochukwu E., Qureshi, Muzaffar, Bell, Zachary I. +2
#FOS: Electrical engineering #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2510.20081

Abstract

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.

Citations

• Convergence Rates of Online Critic Value Function Approximation in Native Spaces
• Lagrangian-based online safe reinforcement learning for state-constrained systems
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• Deep Model Reference Adaptive Control
• Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
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• Applied Optimal Control: Optimization, Estimation, and Control

Related

• State and Input Constrained Output-Feedback Adaptive Optimal Control of Affine Nonlinear Systems
• Confidence-Aware Safe and Stable Control of Control-Affine Systems
• Adaptive Deep Neural Network-Based Control Barrier Functions
• Safety aware model-based reinforcement learning for optimal control of a class of output-feedback nonlinear systems
• Safe Control Under Input Limits with Neural Control Barrier Functions