The Bayesian Separation Principle for Data-driven Control
<|reference_start|>The Bayesian Separation Principle for Data-driven Control: This paper investigates the existence of a separation principle between model identification and control design in the context of model predictive control. First, we elucidate that the separation principle holds asymptotically in the number of data in a Fisherian setting, and universally in a Bayesian setting. Then, by formulating model predictive control within a Gaussian regression framework, we describe how the Bayesian separation principle can be used to derive explicit, uncertainty-aware expressions for the control cost and optimal input sequence, thereby bridging direct and indirect data-driven approaches.<|reference_end|>
@article{grimaldi2024the,
    title={The Bayesian Separation Principle for Data-driven Control},
    author={Riccardo Alessandro Grimaldi, Giacomo Baggio, Ruggero Carli, Gianluigi
  Pillonetto},
    journal={arXiv preprint arXiv:2409.16717},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16717},
    primaryClass={eess.SY cs.SY}
}