Fast Initialization of Control Parameters using Supervised Learning on Data from Similar Assets.

This paper proposes a method to provide a good initialization of control parameters to be found when performing manual or automated control tuning during development, commissioning or periodic retuning. The method is based on treating the initialization problem as a supervised learning one; taking examples from similar machines and similar tasks for which good control parameters have been found, and using those examples to build models that predict good control parameters for new machines and tasks yet to be initialized. Two of such models are proposed, one based on random forest regressors and a second based on neural networks. The random forest is highly data-efficient but generalizes only moderately. The neural network is able to leverage a high-dimensional burner run input to perform automatic system identification and generalization. While the proposed approach can be applied to a variety of applications for which example data from well functioning controllers can be used to hot-start new ones, we applied it in this paper to three slider-crank setups performing a variety of similar tasks. We found that both models outperform a benchmark of using a physics-inspired model for the initialization. Using 20% of the data for training, the required number of experiments was reduced up to 44%, and the performance of the initial experiments was improved by up to 68% compared to the benchmark.