Enhancing direct data-driven model-reference controllers from open loop data: a closed-loop estimation approach

The selection of an appropriate reference model represents a critical decision in direct data-driven model-reference control. In this framework, we introduce a novel approach to estimate the closed-loop behavior of an unknown system in feedback with a given controller using only open-loop data already available. Such an estimation method is then used to build an automatic tuning scheme for model-reference control techniques. The proposed autotuning strategy allows one to select the reference model that best balances user-defined goals and the inevitable limitations of the selected direct control technique, toward the controller’s refinement. We illustrate the effectiveness of our approach through extensive numerical simulations for automatically tuning a VRFT-based controller.