Unraveling the Control Engineer's Craft with Neural Networks
Braghadeesh Lakshminarayanan Federico Dettù Cristian R. Rojas Simone Formentin
Many industrial processes require suitable controllers to meet their
performance requirements. More often, a sophisticated digital twin is
available, which is a highly complex model that is a virtual representation of
a given physical process, whose parameters may not be properly tuned to capture
the variations in the physical process. In this paper, we present a sim2real,
direct data-driven controller tuning approach, where the digital twin is used
to generate input-output data and suitable controllers for several
perturbations in its parameters. State-of-the art neural-network architectures
are then used to learn the controller tuning rule that maps input-output data
onto the controller parameters, based on artificially generated data from
perturbed versions of the digital twin. In this way, as far as we are aware, we
tackle for the first time the problem of re-calibrating the controller by
meta-learning the tuning rule directly from data, thus practically replacing
the control engineer with a machine learning model. The benefits of this
methodology are illustrated via numerical simulations for several choices of
neural-network architectures.