The Virtual Reference Feedback Tuning (VRFT) is a data based method for the design of feedback controllers. In the original formulation, the VRFT method gives a solution to the one degree of freedom model-reference control problem in which the objective is to shape the input-output transfer function of the control system. In this paper, the extension of the method to the design of two degree of freedom controllers is presented and discussed.
This paper presents an extension of the Virtual Reference Feedback Tuning (VRFT) methodology dedicated to linear time-delay systems with known delay and unknown dynamics. The standard VRFT is not well suited for systems with dominant time-delay as it yields high order controllers. The proposed direct approach, relying on a Smith Predictor structure, guarantees the same level of performance as the standard VRFT but with lower order controllers. The joint direct data-driven design of the controller and the predictor is facilitated by the introduction of an ad-hoc optimization initialization. Effectiveness and robustness to uncertainty in the time-delay estimation are shown in a vehicle dynamics control problem.
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