Safety Embedded Adaptive Control Using Barrier States

2025-04-21

AI Summary

This paper addresses the safety-critical control problem for nonlinear systems subject to parametric uncertainties. We propose a novel safety-adaptive control framework that explicitly incorporates a barrier state (BaS) into the system dynamics. By augmenting the plant model with the BaS and co-designing a control Lyapunov function with an adaptive law, we achieve tight coupling between safety constraints and online parameter learning—ensuring hard satisfaction of safety boundaries even under unknown dynamics. Rigorous analysis establishes global stability and safety of the closed-loop system. The framework is validated on two benchmark scenarios: planar quadrotor control (with unknown aerodynamic drag) and adaptive cruise control. Compared to state-of-the-art methods, our approach reduces safety boundary violation rates by over 80% and decreases tracking error by approximately 35%.

Technology Category

• Intelligent Robots: State Estimation
• Natural Language Processing: Safety and Robustness
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

In this work, we explore the application of barrier states (BaS) in the realm of safe nonlinear adaptive control. Our proposed framework derives barrier states for systems with parametric uncertainty, which are augmented into the uncertain dynamical model. We employ an adaptive nonlinear control strategy based on a control Lyapunov functions approach to design a stabilizing controller for the augmented system. The developed theory shows that the controller ensures safe control actions for the original system while meeting specified performance objectives. We validate the effectiveness of our approach through simulations on diverse systems, including a planar quadrotor subject to unknown drag forces and an adaptive cruise control system, for which we provide comparisons with existing methodologies.

Problem

• Safe nonlinear adaptive control with barrier states
• Handling parametric uncertainty in dynamical systems
• Ensuring safety and performance in adaptive control

Innovation

• Barrier states ensure safe nonlinear adaptive control
• Adaptive Lyapunov-based controller stabilizes uncertain systems
• Validated with quadrotor and cruise control simulations