Measured-state conditioned recursive feasibility for stochastic model predictive control
<|reference_start|>Measured-state conditioned recursive feasibility for stochastic model predictive control: In this paper, we address the problem of designing stochastic model predictive control (MPC) schemes for linear systems affected by unbounded disturbances. The contribution of the paper is twofold. First, motivated by the difficulty of guaranteeing recursive feasibility in this framework, due to the nonzero probability of violating chance-constraints in the case of unbounded noise, we introduce the novel definition of measured-state conditioned recursive feasibility in expectation. Second, we construct a stochastic MPC scheme, based on the introduction of ellipsoidal probabilistic reachable sets, which implements a closed-loop initialization strategy, i.e., the current measured-state is employed for initializing the optimization problem. This new scheme is proven to satisfy the novel definition of recursive feasibility, and its superiority with respect to open-loop initialization schemes, arising from the fact that one never neglects the information brought by the current measurement, is shown through numerical examples.<|reference_end|>
@article{fiacchini2024measured-state,
    title={Measured-state conditioned recursive feasibility for stochastic model
  predictive control},
    author={Mirko Fiacchini, Martina Mammarella and Fabrizio Dabbene},
    journal={arXiv preprint arXiv:2406.13522},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13522},
    primaryClass={eess.SY cs.SY math.OC}
}