Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Data-driven optimal control of bilinear systems
Z. Yuan, J. Cortés
IEEE Control Systems Letters 6 (2022), 2479-2484
Abstract
This paper develops a method to learn optimal
controls from data for bilinear systems without a
priori knowledge of the system dynamics. Given an
unknown bilinear system, we first characterize when
the available data is sufficiently informative to
solve the optimal control problem. This
characterization leads us to propose an online
control experiment design procedure that guarantees
that any input/state trajectory can be represented
as a linear combination of collected input/state
data matrices. Leveraging this data-based
representation, we transform the original optimal
control problem into an equivalent data-based
optimization problem with bilinear constraints. We
solve the latter by iteratively employing a
convex-concave procedure to convexify it and find a
locally optimal control sequence. Simulations show
that the performance of the proposed data-based
approach is comparable with model-based methods.
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Mechanical and Aerospace Engineering,
University of California, San Diego
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cortes at ucsd.edu
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