Safety-Critical Adaptive Control with Nonlinear Reference Model Systems.

Abstract

In this paper, a model reference adaptive control architecture is proposed for uncertain nonlinear systems to achieve prescribed performance guarantees. Specifically, a general nonlinear reference model system is considered that captures an ideal and safe system behavior. An adaptive control architecture is then proposed to suppress the effects of system uncertainties without any prior knowledge of their magnitude and rate upper bounds. More importantly, the proposed control architecture enforces the system state trajectories to evolve within a user-specified prescribed distance from the reference system trajectories, satisfying the safety constraints. This eliminates the ad-hoc tuning process for the adaptation rate that is conventionally required in model reference adaptive control to ensure safety. The efficacy of the proposed control architecture is also demonstrated through an illustrative numerical example.

Bib

@inproceedings{DBLP:conf/amcc/ArabiGP20,
  author       = {Ehsan Arabi and
                  Kunal Garg and
                  Dimitra Panagou},
  title        = {Safety-Critical Adaptive Control with Nonlinear Reference Model Systems},
  booktitle    = {2020 American Control Conference, {ACC} 2020, Denver, CO, USA, July
                  1-3, 2020},
  pages        = {1749--1754},
  publisher    = {{IEEE}},
  year         = {2020},
  url          = {https://doi.org/10.23919/ACC45564.2020.9147999},
  doi          = {10.23919/ACC45564.2020.9147999},
  timestamp    = {Sun, 08 Aug 2021 01:40:57 +0200},
  biburl       = {https://dblp.org/rec/conf/amcc/ArabiGP20.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}