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Data-Driven Min-Max Moving Horizon Estimation
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DOI：10.1109/tac.2026.3730322.png)
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Abstract
En 中文
This manuscript proposes a data-driven min-max moving horizon estimation (MHE) method for state estimation of linear finite-dimensional time-varying systems with unknown parameters and bounded-and-unknown plant and measurement noises/disturbances. A set membership representation is leveraged to characterize the unknown system parameters using offline measured data. We show that the robust MHE formulation of the online state estimation problem can be relaxed to a convex semidefinite program problem subject to constraints of linear matrix inequalities. The proposed design is able to consider both the measurement and process noise and handle state constraints. The stability analysis of the state estimation error is further provided. Finally, a numerical example is provided to verify the effectiveness of the proposed method.
Keywords:
Chemical process control
data-driven state estimation
moving horizon estimation
observers of linear systems
state estimation
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7
Papers:
1.3W
Citations:
6.7W
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