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Data-Efficient Autotuning With Bayesian Optimization: An Industrial Control Study
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DOI：10.1109/TCST.2018.2886159.png)
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Abstract
En 中文
Bayesian optimization (BO) 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 BO 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 autotuning method is shown to outperform manual calibration: it consistently achieves better performance with a low number of experiments. The proposed autotuning framework is flexible and can handle different control structures and objectives.
Keywords:
Tuning
Probabilistic logic
Valves
Bayes methods
Linear programming
Industries
Optimization
Automatic controller tuning
Bayesian optimization (BO)
industry control
learning control
machine learning
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