Learning-based Prescribed-Time Safety for Control of Unknown Systems with Control Barrier Functions

2024/03/12 by Tzu-Yuan Huang, Sihua Zhang, Huang, Tzu-Yuan +11 · 3 citations
Engineering · #Advanced Control Systems Optimization #Advanced Data Processing Techniques #FOS: Electrical engineering #Fault Detection and Control Systems #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2403.08054

openalex publication_date 2024/03/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03

Abstract

In many control system applications, state constraint satisfaction needs to be guaranteed within a prescribed time. While this issue has been partially addressed for systems with known dynamics, it remains largely unaddressed for systems with unknown dynamics. In this paper, we propose a Gaussian process-based time-varying control method that leverages backstepping and control barrier functions to achieve safety requirements within prescribed time windows for control affine systems. It can be used to keep a system within a safe region or to make it return to a safe region within a limited time window. These properties are cemented by rigorous theoretical results. The effectiveness of the proposed controller is demonstrated in a simulation of a robotic manipulator.

Cited by

• SAD-Flower: Flow Matching for Safe, Admissible, and Dynamically Consistent Planning
• A Data-Driven Prescribed-Time Control Framework via Koopman Operator and Adaptive Backstepping
• Constant-Sum High-Order Barrier Functions for Safety Between Parallel Boundaries

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