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.

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

Albi, Giacomo;
2022

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.
Training
Supervised learning
Feedback control
Riccati equations
Nonlinear dynamical systems
Data models
Real-time systems
Nonlinear feedback control
state-dependent Riccati equations
supervised learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1055838
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