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

TL;DR

This paper introduces a robust adaptive control barrier function approach that manages time-varying constraints in robotic surface treatment, effectively handling model uncertainties and disturbances to ensure consistent quality.

Contribution

It proposes a novel RaCBF-based control method with set membership identification for robust, adaptive, time-varying constraint enforcement in uncertain robotic systems.

Findings

Successfully applied to robotic surface treatment with real robot experiments

Guarantees uniform quality within acceptable bounds

Reduces conservatism in robustness through set membership identification

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.