Book ; Online: Gradient-augmented Supervised Learning of Optimal Feedback Laws Using State-dependent Riccati Equations
2021
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 ... ...
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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Keywords | Mathematics - Optimization and Control ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Systems and Control |
Publishing date | 2021-03-06 |
Publishing country | us |
Document type | Book ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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