Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study

Abstract

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.

11 pages, 7 figures and 4 tables. To appear in IEEE Transactions on Control Systems Technology

References in corpus (8)

• Practical Bayesian Optimization of Machine Learning Algorithms
• Gaussian Processes for Data-Efficient Learning in Robotics and Control
• Bayesian Optimization with Unknown Constraints
• Safe Controller Optimization for Quadrotors with Gaussian Processes
• Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization
• Automatic LQR Tuning Based on Gaussian Process Global Optimization
• On the Design of LQR Kernels for Efficient Controller Learning
• Gait learning for soft microrobots controlled by light fields

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