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arXiv · 2103.04091

Gradient-augmented Supervised Learning of Optimal Feedback Laws Using State-dependent Riccati Equations

Giacomo Albi·Sara Bicego·Dante Kalise
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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 solves. The training phase is enriched by the use 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 solves can be substituted by a suitably trained feedforward neural network.

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Giacomo Albi, Sara Bicego, Dante Kalise. 2021-03-06. Gradient-augmented Supervised Learning of Optimal Feedback Laws Using State-dependent Riccati Equations. https://arxiv.org/abs/2103.04091

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