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

• URL: http://arxiv.org/abs/2103.04091v1
• Date: Sat, 6 Mar 2021 10:34:23 GMT
• Title: Gradient-augmented Supervised Learning of Optimal Feedback Laws Using State-dependent Riccati Equations
• Authors: Giacomo Albi, Sara Bicego, Dante Kalise
• Abstract summary: A stabilizing feedback law is trained from a dataset generated from State-dependent Riccati Equation solves. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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