Optimal input design for direct data-driven tuning of model-reference controllers

In recent years, direct data-driven controller tuning methods have been proposed as an alternative to the standard model-based approach for model-reference control design. In this work, the problem of input design for noniterative direct data-driven techniques, namely Virtual Reference Feedback Tuning (VRFT) and noniterative Correlation-based Tuning (CbT), is investigated. For bounded input energy, the excitation signal is designed such that the expected value of the considered control cost is reduced. The above strategy is numerically tested on a benchmark example.