Controlled Invariant Sets for Gaussian Process State Space Models
Systems and Control
2026-04-21 v2 Systems and Control
Abstract
We compute probabilistic controlled invariant sets for nonlinear systems using Gaussian process state space models, which are data-driven models that account for unmodeled and unknown nonlinear dynamics. We propose a semidefinite programming scheme for designing state-feedback controllers that maximize the probability of the trajectories staying within a probabilistic controlled invariant set while satisfying input constraints. The results are validated on a quadrotor, both in simulation and on a physical platform.
Cite
@article{arxiv.2407.11256,
title = {Controlled Invariant Sets for Gaussian Process State Space Models},
author = {Paul Griffioen and Bingzhuo Zhong and Murat Arcak and Majid Zamani and Marco Caccamo},
journal= {arXiv preprint arXiv:2407.11256},
year = {2026}
}