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.

中文翻译：

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贝叶斯优化的数据有效自动调整：工业控制研究

提出了贝叶斯优化（BO），用于从实验数据中自动学习最佳控制器参数。概率描述（高斯过程）用于从控制器参数到用户定义成本的未知函数建模。用数据更新概率模型，该数据是通过在物理系统上测试一组参数并评估成本而获得的。为了快速学习，BO算法选择下一个参数以系统的方式进行评估，例如，通过最大化有关最佳值的信息增益。因此，该算法只需很少的实验就可以迭代地找到全局最优参数。以节气门控制为代表的工业控制示例，所提出的自动调整方法显示出优于手动校准的方法：通过少量的实验，它始终可以达到更好的性能。提出的自动调整框架非常灵活，可以处理不同的控制结构和目标。