Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on a probabilistic description of the latent objective function, typically a Gaussian process (GP). As is shown herein, GPs with a common kernel choice can, however, lead to poor learning outcomes on standard quadratic control problems. For a first-order system, we construct two kernels that specifically leverage the structure of the well-known Linear Quadratic Regulator (LQR), yet retain the flexibility of Bayesian nonparametric learning. Simulations of uncertain linear and nonlinear systems demonstrate that the LQR kernels yield superior learning performance.
| Author(s): | Alonso Marco and Philipp Hennig and Stefan Schaal and Sebastian Trimpe |
| Book Title: | Proceedings of the 56th IEEE Conference on Decision and Control |
| Year: | 2017 |
| Month: | December |
| Department(s): | Autonomous Motion, Probabilistic Numerics |
| Research Project(s): |
Automatic Controller Tuning using Bayesian Optimization
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| Bibtex Type: | Conference Paper (conference) |
| Paper Type: | Conference |
| State: | Accepted |
| Links: |
arXiv
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BibTex @conference{MaHeScTr17,
title = {On the Design of {LQR} Kernels for Efficient Controller Learning},
author = {Marco, Alonso and Hennig, Philipp and Schaal, Stefan and Trimpe, Sebastian},
booktitle = {Proceedings of the 56th IEEE Conference on Decision and Control},
month = dec,
year = {2017},
month_numeric = {12}
}
|
|
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