Robust Control Barrier and Control Lyapunov Functions with Fixed-Time Convergence Guarantees.

Abstract

This paper studies control synthesis for a general class of nonlinear, control-affine dynamical systems under additive disturbances and state-estimation errors. We enforce forward invariance of static and dynamic safe sets and convergence to a given goal set within a user-defined time in the presence of input constraints. We use robust variants of control barrier functions (CBF) and fixed-time control Lyapunov functions (FxT-CLF) to incorporate a class of additive disturbances in the system dynamics, and state-estimation errors. To solve the underlying constrained control problem, we formulate a quadratic program and use the proposed robust CBF-FxT-CLF conditions to compute the control input. We showcase the efficacy of the proposed method on a numerical case study involving multiple underactuated marine vehicles.

Bib

@inproceedings{DBLP:conf/amcc/GargP21a,
  author       = {Kunal Garg and
                  Dimitra Panagou},
  title        = {Robust Control Barrier and Control Lyapunov Functions with Fixed-Time
                  Convergence Guarantees},
  booktitle    = {2021 American Control Conference, {ACC} 2021, New Orleans, LA, USA,
                  May 25-28, 2021},
  pages        = {2292--2297},
  publisher    = {{IEEE}},
  year         = {2021},
  url          = {https://doi.org/10.23919/ACC50511.2021.9482751},
  doi          = {10.23919/ACC50511.2021.9482751},
  timestamp    = {Thu, 14 Oct 2021 10:23:01 +0200},
  biburl       = {https://dblp.org/rec/conf/amcc/GargP21a.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}