ベイズ最適化によるデータ効率的な自動チューニング：産業制御への応用
Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study
ベイズ最適化を用いて実験データから制御器パラメータを自動学習する手法を提案し、スロットルバルブ制御で手動調整より少ない試行で高性能を達成した。
著者: Matthias Neumann-Brosig, Alonso Marco, Dieter Schwarzmann, Sebastian Trimpe
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
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.
