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Robust data-driven min–max model predictive control with unknown-input observers
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DOI：10.1016/j.automatica.2026.113010.png)
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Abstract
En 中文
Controlling unknown linear time-invariant systems in the presence of unknown process disturbances presents considerable challenges, particularly in accurately estimating the system states from noisy output measurements as well as managing the interplay between unknown disturbances and data. To address these challenges, this paper proposes a novel robust data-driven output feedback model predictive control (MPC) framework. Central to the proposed method is the integration of a data-driven unknown-input observer (UIO) with a data-driven min–max MPC scheme. The data-driven UIO provides real-time state estimates of the unknown system and ensures the asymptotic convergence of estimation errors in the presence of unknown disturbances. These estimates are then used within the data-driven UIO-based min–max MPC framework, which employs a data-based semi-definite program (SDP) to compute optimal control inputs, effectively mitigating the impact of process disturbances. It is further shown that the proposed approach guarantees recursive feasibility and robust stability of the closed-loop system, as demonstrated through numerical simulations.
Keywords:
data-driven control
unknown-input observer
model predictive control
robust stability
system identification
Journal
IF:
5.9
Papers:
1.2W
Citations:
5.2W
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