@article{markolf2022recedinghorizon,
  abstract = {This work studies receding-horizon control of discrete-time switched linear systems subject to polytopic constraints for the continuous states and inputs. The objective is to approximate the optimal receding-horizon control strategy for cases in which the online computation is intractable due to the necessity of solving mixed-integer quadratic programs in each discrete time instant. The proposed approach builds upon an approximated optimal finite-horizon control law in closed-loop form with guaranteed constraint satisfaction. The paper derives the properties of recursive feasibility and asymptotic stability for the proposed approach. A numerical example is provided for illustration and evaluation of the approach.},
  author = {Markolf, Lukas and Stursberg, Olaf},
  doi = {https://doi.org/10.1007/s42979-022-01442-0},
  interhash = {7ca72057978a6df9f5d9212801dc9844},
  intrahash = {3c78e858a562a35db3a3946a4b4927f8},
  journal = {SN Computer Science},
  number = 62,
  title = {Receding-Horizon Control of Constrained Switched Systems with Neural Networks as Parametric Function Approximators},
  url = {https://link.springer.com/article/10.1007/s42979-022-01442-0#author-information},
  volume = 4,
  year = 2022
}