Learning-based Parameterized Barrier Function for Safety-Critical Control of Unknown Systems

TL;DR

This paper introduces a learning-based control approach using Gaussian process regression and parameterized high-order control barrier functions to ensure safety in systems with unknown disturbances, improving flexibility and feasibility over existing methods.

Contribution

It proposes a novel GP-based parameterized control barrier function method that reduces conservatism and enhances safety guarantees for unknown systems.

Findings

Effective safety assurance demonstrated on robotic manipulator simulations.

The method offers improved feasibility compared to traditional robust safety approaches.

Prediction error bounds guarantee the control policy's safety performance.

Abstract

With the increasing complexity of real-world systems and varying environmental uncertainties, it is difficult to build an accurate dynamic model, which poses challenges especially for safety-critical control. In this paper, a learning-based control policy is proposed to ensure the safety of systems with unknown disturbances through control barrier functions (CBFs). First, the disturbance is predicted by Gaussian process (GP) regression, whose prediction performance is guaranteed by a deterministic error bound. Then, a novel control strategy using GP-based parameterized high-order control barrier functions (GP-P-HOCBFs) is proposed via a shrunk original safe set based on the prediction error bound. In comparison to existing methods that involve adding strict robust safety terms to the HOCBF condition, the proposed method offers more flexibility to deal with the conservatism and the feasibility of solving quadratic problems within the CBF framework. Finally, the effectiveness of the proposed method is demonstrated by simulations on Franka Emika manipulator.