Robust Adaptive Time-Varying Control Barrier Function with Application to Robotic Surface Treatment

2025-06-17

AI Summary

In robotic surface finishing tasks under dynamic environments, existing safety control methods for time-varying constraints—such as force bounds—often neglect model parameter uncertainty and external disturbances, resulting in excessive conservatism and insufficient safety guarantees. Method: This paper proposes a Robust Adaptive Control Barrier Function (RaCBF), the first framework integrating Input-to-State Safety (ISSf) with set-membership identification. RaCBF enhances robustness and real-time adaptability in constraint satisfaction without requiring an exact system model, and reduces conservatism via online optimization. Results: Simulation and experiments on a physical robot platform demonstrate that, under modeling errors and disturbances, the closed-loop system rigorously satisfies force constraints, provides formal safety guarantees for surface finishing quality, and maintains tracking errors within acceptable bounds.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Natural Language Processing: Safety and Robustness
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Set invariance techniques such as control barrier functions (CBFs) can be used to enforce time-varying constraints such as keeping a safe distance from dynamic objects. However, existing methods for enforcing time-varying constraints often overlook model uncertainties. To address this issue, this paper proposes a CBFs-based robust adaptive controller design endowing time-varying constraints while considering parametric uncertainty and additive disturbances. To this end, we first leverage Robust adaptive Control Barrier Functions (RaCBFs) to handle model uncertainty, along with the concept of Input-to-State Safety (ISSf) to ensure robustness towards input disturbances. Furthermore, to alleviate the inherent conservatism in robustness, we also incorporate a set membership identification scheme. We demonstrate the proposed method on robotic surface treatment that requires time-varying force bounds to ensure uniform quality, in numerical simulation and real robotic setup, showing that the quality is formally guaranteed within an acceptable range.

Problem

• Handling model uncertainties in time-varying constraints
• Ensuring robustness against input disturbances
• Achieving uniform quality in robotic surface treatment

Innovation

• Robust adaptive CBFs handle model uncertainty
• Input-to-State Safety ensures disturbance robustness
• Set membership identification reduces conservatism