Stochastic Data-Driven Predictive Control: Chance-Constraint Satisfaction with Identified Multi-step Predictors
<|reference_start|>Stochastic Data-Driven Predictive Control: Chance-Constraint Satisfaction with Identified Multi-step Predictors: We propose a novel data-driven stochastic model predictive control framework for uncertain linear systems with noisy output measurements. Our approach leverages multi-step predictors to efficiently propagate uncertainty, ensuring chance constraint satisfaction with minimal conservatism. In particular, we present a strategy to identify multi-step predictors and quantify the associated uncertainty using a surrogate (data-driven) state space model. Then, we utilize the derived distribution to formulate a constraint tightening that ensures chance constraint satisfaction. A numerical example highlights the reduced conservatism of handling parametric uncertainty in the proposed method compared to state-of-the-art solutions.<|reference_end|>
@article{balim2024stochastic,
    title={Stochastic Data-Driven Predictive Control: Chance-Constraint
  Satisfaction with Identified Multi-step Predictors},
    author={Haldun Balim, Andrea Carron, Melanie N. Zeilinger, Johannes K"ohler},
    journal={arXiv preprint arXiv:2409.10405},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10405},
    primaryClass={eess.SY cs.SY math.OC}
}