Data-efficient Autotuning with Bayesian Optimization: An Industrial Control Study
2020
Article
ics
Bayesian optimization is proposed for automatic learning of optimal controller parameters from experimental data. A probabilistic description (a Gaussian process) is used to model the unknown function from controller parameters to a user-defined cost. The probabilistic model is updated with data, which is obtained by testing a set of parameters on the physical system and evaluating the cost. In order to learn fast, the Bayesian optimization algorithm selects the next parameters to evaluate in a systematic way, for example, by maximizing information gain about the optimum. The algorithm thus iteratively finds the globally optimal parameters with only few experiments. Taking throttle valve control as a representative industrial control example, the proposed auto-tuning method is shown to outperform manual calibration: it consistently achieves better performance with a low number of experiments. The proposed auto-tuning framework is flexible and can handle different control structures and objectives.
Author(s): | Matthias Neumann-Brosig and Alonso Marco and Dieter Schwarzmann and Sebastian Trimpe |
Journal: | IEEE Transactions on Control Systems Technology |
Volume: | 28 |
Number (issue): | 3 |
Pages: | 730--740 |
Year: | 2020 |
Month: | May |
Department(s): | Intelligent Control Systems |
Research Project(s): |
Controller Learning using Bayesian Optimization
|
Bibtex Type: | Article (article) |
Paper Type: | Journal |
DOI: | 10.1109/TCST.2018.2886159 |
State: | Published |
Links: |
arXiv (PDF)
|
BibTex @article{NeuMarSchTri18, title = {Data-efficient Autotuning with Bayesian Optimization: An Industrial Control Study}, author = {Neumann-Brosig, Matthias and Marco, Alonso and Schwarzmann, Dieter and Trimpe, Sebastian}, journal = {IEEE Transactions on Control Systems Technology}, volume = {28}, number = {3}, pages = {730--740}, month = may, year = {2020}, doi = {10.1109/TCST.2018.2886159}, month_numeric = {5} } |