A Comparative Study of Data-Driven Control Tuning: VRFT and FRIT for DC Motor Speed Regulation

Abstract

This study compares two data-driven control tuning methods Virtual Reference Feedback Tuning (VRFT), and Fictitious Reference Iterative Tuning (FRIT) applied to a DC motor speed control system. Both methods aim to achieve a predefined closed-loop behaviour without explicit plant modelling, relying instead on measured input–output data. For the VRFT method, single-shot open-loop data were collected using a PRBS signal to excite the DC motor, while FRIT used a single-shot closed-loop experiment under an initial PI controller. Each method used the same reference model, a first-order system with a 2 second time constant, to guide the tuning process. The VRFT approach produced a 6-DOF controller through least-squares optimization, whereas the FRIT method refined the parameters of a PI controller by minimizing a defined cost function. Simulations conducted at target speeds of 60 and 100 RPM demonstrated that both controllers delivered comparable tracking performance, despite having different structural designs. These findings confirm that both VRFT and FRIT can generate effective control strategies from limited data, providing design flexibility while still achieving the desired closed-loop behaviour.