Towards safe and tractable Gaussian process-based MPC: Efficient sampling within a sequential quadratic programming framework
<|reference_start|>Towards safe and tractable Gaussian process-based MPC: Efficient sampling within a sequential quadratic programming framework: Learning uncertain dynamics models using Gaussian process~(GP) regression has been demonstrated to enable high-performance and safety-aware control strategies for challenging real-world applications. Yet, for computational tractability, most approaches for Gaussian process-based model predictive control (GP-MPC) are based on approximations of the reachable set that are either overly conservative or impede the controller's safety guarantees. To address these challenges, we propose a robust GP-MPC formulation that guarantees constraint satisfaction with high probability. For its tractable implementation, we propose a sampling-based GP-MPC approach that iteratively generates consistent dynamics samples from the GP within a sequential quadratic programming framework. We highlight the improved reachable set approximation compared to existing methods, as well as real-time feasible computation times, using two numerical examples.<|reference_end|>
@article{prajapat2024towards,
    title={Towards safe and tractable Gaussian process-based MPC: Efficient
  sampling within a sequential quadratic programming framework},
    author={Manish Prajapat, Amon Lahr, Johannes K"ohler, Andreas Krause, Melanie
  N. Zeilinger},
    journal={arXiv preprint arXiv:2409.08616},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08616},
    primaryClass={math.OC cs.LG cs.SY eess.SY}
}