Online Learning of a Regularization Parameter for Data-Driven Predictive Control under Noisy Conditions

Abstract

In this paper, we propose a method for tuning a regularization parameter in data-driven predictive control considering noise. The proposed method uses the error between the predictions obtained from the optimization problem and the actual measurements as an indicator, and adjusts the regularization parameter while controlling the system. The effectiveness of the proposed method is confirmed through numerical examples.