Safe Output-Feedback Adaptive Optimal Control of Affine Nonlinear Systems

TL;DR

This paper presents a novel safe control framework that combines adaptive optimal control and control barrier functions, ensuring safety and regulation for affine nonlinear systems even with partial state measurements, using neural network-based observers.

Contribution

It introduces a decoupled control architecture integrating CBFs with AOC, robustified for partial measurements, and leverages neural network observers for safety guarantees.

Findings

Guarantees safety despite partial state information.

Demonstrates effectiveness through simulation.

Provides Lyapunov-based safety and convergence analysis.

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.