Data-Driven Controlled Invariant Sets for Gaussian Process State Space Models
<|reference_start|>Data-Driven Controlled Invariant Sets for Gaussian Process State Space Models: 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 investigate the relationship between robust and probabilistic invariance, leveraging this relationship to design state-feedback controllers that maximize the probability of the system staying within the probabilistic controlled invariant set. We propose a semi-definite-programming-based optimization scheme for designing the state-feedback controllers subject to input constraints. The effectiveness of our results are demonstrated and validated on a quadrotor, both in simulation and on a physical platform.<|reference_end|>
@article{griffioen2024data-driven,
    title={Data-Driven Controlled Invariant Sets for Gaussian Process State Space
  Models},
    author={Paul Griffioen, Bingzhuo Zhong, Murat Arcak, Majid Zamani, Marco
  Caccamo},
    journal={arXiv preprint arXiv:2407.11256},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11256},
    primaryClass={eess.SY cs.SY}
}