Gradient-augmented supervised learning of optimal feedback laws using state-dependent Riccati equations
File(s)
Accepted version
Author(s)
Albi, Giacomo
Bicego, Sara
Kalise, Dante
Type
Journal Article
Abstract
A supervised learning approach for the solution of large-scale nonlinear stabilization problems is presented. A stabilizing feedback law is trained from a dataset generated from State-dependent Riccati Equation solvers. The training phase is enriched by the use of gradient information in the loss function, which is weighted through the use of hyperparameters. High-dimensional nonlinear stabilization tests demonstrate that real-time sequential large-scale Algebraic Riccati Equation solvers can be substituted by a suitably trained feedforward neural network.
Date Issued
2021-06-04
Date Acceptance
2021-05-29
Citation
IEEE Control Systems Letters, 2021, 6, pp.836-841
ISSN
2475-1456
Publisher
Institute of Electrical and Electronics Engineers
Start Page
836
End Page
841
Journal / Book Title
IEEE Control Systems Letters
Volume
6
Copyright Statement
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Subjects
math.OC
math.OC
cs.LG
cs.SY
eess.SY
Publication Status
Published