@article{63476,
  abstract     = {{<jats:title>ABSTRACT</jats:title><jats:p>We develop a three‐component Model Predictive Control (MPC) algorithm to achieve output‐reference tracking with prescribed performance for continuous‐time nonlinear systems. One component is the so‐called funnel MPC, which achieves reference tracking with prescribed performance for the model output for suitable models. Recently, this MPC algorithm has been combined with a model‐free reactive feedback controller (second component) to account for model‐plant mismatches, bounded disturbances, and uncertainties. By construction, this two‐component controller defines a robust funnel MPC algorithm. It achieves output‐reference tracking within prescribed bounds on the tracking error for a class of unknown nonlinear systems. In this paper, we extend the robust funnel MPC by a machine learning component to adapt the underlying model to the system data and, thus, improve the contribution of MPC. We derive sufficient structural conditions to define a class of models for funnel MPC, and provide a characterization of suitable learning schemes. Since robust funnel MPC is inherently robust and the evolution of the tracking error in the prescribed performance funnel is guaranteed, the additional learning component can perform the learning task online—even without an initial model or offline training.</jats:p>}},
  author       = {{Lanza, Lukas and Dennstädt, Dario and Berger, Thomas and Worthmann, Karl}},
  issn         = {{1049-8923}},
  journal      = {{International Journal of Robust and Nonlinear Control}},
  number       = {{13}},
  pages        = {{5569--5582}},
  publisher    = {{Wiley}},
  title        = {{{Safe Continual Learning in Model Predictive Control With Prescribed Bounds on the Tracking Error}}},
  doi          = {{10.1002/rnc.8001}},
  volume       = {{35}},
  year         = {{2025}},
}

@article{35580,
  author       = {{Schulze Darup, Moritz}},
  issn         = {{1049-8923}},
  journal      = {{International Journal of Robust and Nonlinear Control}},
  keywords     = {{Electrical and Electronic Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering, Aerospace Engineering, Biomedical Engineering, General Chemical Engineering, Control and Systems Engineering}},
  number       = {{11}},
  pages        = {{4168--4187}},
  publisher    = {{Wiley}},
  title        = {{{Encrypted polynomial control based on tailored two‐party computation}}},
  doi          = {{10.1002/rnc.5003}},
  volume       = {{30}},
  year         = {{2020}},
}

@article{35585,
  author       = {{Lu, Jingyi and Leong, Alex S. and Quevedo, Daniel E.}},
  issn         = {{1049-8923}},
  journal      = {{International Journal of Robust and Nonlinear Control}},
  keywords     = {{Electrical and Electronic Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering, Aerospace Engineering, Biomedical Engineering, General Chemical Engineering, Control and Systems Engineering}},
  number       = {{11}},
  pages        = {{4205--4224}},
  publisher    = {{Wiley}},
  title        = {{{Optimal event‐triggered transmission scheduling for privacy‐preserving wireless state estimation}}},
  doi          = {{10.1002/rnc.4910}},
  volume       = {{30}},
  year         = {{2020}},
}