Inverse supervised learning of controller tuning rules

In this technical communique, we present a sim2real approach for data-driven controller tuning, utilizing a digital twin to generate input–output data and suitable controllers around nominal parameter values. We establish a direct inverse supervised learning framework using advanced neural network architectures, including the WaveNet sequence model, to learn a tuning rule that maps input–output data to controller parameters. This approach automates controller re-calibration by meta-learning the tuning rule through inverse supervised learning, effectively avoiding human intervention via a machine learning model. The advantages of this methodology are demonstrated through numerical simulations across various neural network architectures.