
The core message of this paper is to propose a data-driven min-max model predictive control (MPC) scheme that can robustly stabilize an unknown linear time-invariant (LTI) system and satisfy input and state constraints, even in the presence of process noise. The authors further propose an adaptive data-driven min-max MPC scheme that exploits additional online input-state data to improve closed-loop performance.


coremsg

robust-and-adaptive-data-driven-model-predictive-control-for-unknown-linear-systems


Robust and Adaptive Data-Driven Model Predictive Control for Unknown Linear Systems
