Saccani, DaniloFerrari-Trecate, GiancarloZeilinger, Melanie N.Kohler, Johannes2024-02-202024-02-202024-02-202023-01-0110.1109/LCSYS.2023.3336261https://infoscience.epfl.ch/handle/20.500.14299/204808WOS:001129117400007We present a robust model predictive control (MPC) framework for linear systems facing bounded parametric uncertainty and bounded disturbances. Our approach deviates from standard MPC formulations by integrating multi-step predictors, which provide reduced error bounds. These bounds, derived from multi-step predictors, are utilized in a homothetic tube formulation to mitigate conservatism. Lastly, a multi-rate formulation is adopted to handle the incompatibilities of multi-step predictors. We provide a theoretical analysis, guaranteeing robust recursive feasibility, constraint satisfaction, and (practical) stability of the desired setpoint. We use a simulation example to compare it to existing literature and demonstrate advantages in terms of conservatism and computational complexity.TechnologyPredictive Control For Linear SystemsIdentification For ControlConstrained Controltext::journal::journal article::research article