Data-driven dynamic optimal allocation for uncertain over-actuated linear systems

Sergio Galeani

doi:10.1016/j.automatica.2025.112208

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Data-driven dynamic optimal allocation for uncertain over-actuated linear systems

2025-05-01

Sergio Galeani

Roberto Masocco *

Mario Sassano

DOI：10.1016/j.automatica.2025.112208

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Abstract

En 中文

The dynamic control allocation problem for LTI systems is addressed in an uncertain setting. In the presence of unstructured uncertainties affecting the underlying plant, a completely data-driven strategy is envisioned to optimally allocate the control action in the presence of non-constant steadystate behavior of the plant, while leaving untouched the regulated output response induced by an a priori given controller. Compared with the current state of the art, the proposed solution exhibits several appealing features. Such features are: complete invisibility of the allocator's action (after a training interval if the plant is unknown), exact optimization of the periodic steady-state evolution, arbitrary speed of the allocation action; while all of them are achieved even for unknown plants in this paper, in the current literature they are impossible to achieve or just obtainable for a perfectly known plant. (c) 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Keywords:

Dynamic control allocation

Data-driven methods

Linear systems

Lagrange functional

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