Data driven Model Free Adaptive Control Tuned by Virtual Reference Feedback Tuning
This paper proposes a new tuning approach, by which, all parameters of a data driven Model Free Adaptive Control (MFAC) algorithm are automatically determined using a nonlinear Virtual Reference Feedback Tuning (VRFT) algorithm. The approach is referred to as mixed MFAC VFRT control and it leads to mixed MFAC VFRT algorithms. An advantage of mixed MFAC VFRT control, is that it combines systematically, the features of VRFT (it computes the controller parameters using only the input/output data) with those of MFAC. This is especially illustrated by comparison with the classical MFAC algorithms, the initial values of the parameters, which are obtained through a process that involves solving an optimization problem. The application that validates the mixed MFAC VFRT algorithms, by experiment, is a nonlinear twin rotor aerodynamic system laboratory equipment position control system, that represents a tribute, to Prof. Antal (Tony) K. Bejczy for his excellent results in space robotics, robot dynamics and control, haptics and force perception/control.