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, Intelligent Control Systems, Probabilistic Numerics |
Research Project(s): |
Automatic Controller Tuning using Bayesian Optimization
|
Bibtex Type: | Conference Paper (conference) |
Paper Type: | Conference |
State: | Accepted |
Links: |
arXiv
|
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} } |