Safe Adaptive Control for Uncertain Systems with Complex Input Constraints

In this paper, we propose a novel adaptive Control Barrier Function (CBF) based controller for nonlinear systems with complex, time-varying input constraints. Conventional CBF approaches often struggle with feasibility issues and stringent assumptions when addressing input constraints. Unlike these...

Full description

Saved in:
Bibliographic Details
Main Authors Deng, Yaosheng, Bai, Yang, Wang, Yujie, Ogura, Masaki, Feroskhan, Mir
Format Journal Article
LanguageEnglish
Published 18.08.2024
Subjects
Computer Science - Systems and Control
Online AccessGet full text

Cover

More Information
Summary:In this paper, we propose a novel adaptive Control Barrier Function (CBF) based controller for nonlinear systems with complex, time-varying input constraints. Conventional CBF approaches often struggle with feasibility issues and stringent assumptions when addressing input constraints. Unlike these methods, our approach converts the input-constraint problem into an output-constraint CBF design. This transformation simplifies the Quadratic Programming (QP) formulation and enhances compatibility with the CBF framework. We design an adaptive CBF-based controller to manage the mismatched uncertainties introduced by this transformation. Our method systematically addresses the challenges of complex, time-varying, and state-dependent input constraints. The efficacy of the proposed approach is validated using numerical examples.
DOI:10.48550/arxiv.2408.09534