2002
DOI: 10.1002/acs.711
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Virtual reference feedback tuning for two degree of freedom controllers

Abstract: 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.

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Cited by 135 publications
(68 citation statements)
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References 29 publications
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“…Graham et al [3] reported an application of IFT to a mechanical process. Lequin et al [6] proposed VRFT for controllers with two degrees of freedom. Campi et al [2] introduced the VRFT approach for controller tuning in a nonlinear environment.…”
Section: Introductionmentioning
confidence: 99%
“…Graham et al [3] reported an application of IFT to a mechanical process. Lequin et al [6] proposed VRFT for controllers with two degrees of freedom. Campi et al [2] introduced the VRFT approach for controller tuning in a nonlinear environment.…”
Section: Introductionmentioning
confidence: 99%
“…It also performs many experiments to update the controller parameters to minimize the performance index, so that it is not effective nor efficient to tune controller parameters, because it takes several times and costs. Campi et al [19] proposed virtual reference feedback tuning (VRFT); Lecchini et al [20] proposed 2DOF VRFT; Rojas et al [21] proposed a feedforward formulation of the VRFT method based on a 2DOF control configuration; and Gazdos et al [22] proposed a VRFT method for iterative controller design and fine tuning. VRFT uses a set of measured input/output data for the design of a controller with the desired structure, but without restrictions on data generation.…”
Section: Introductionmentioning
confidence: 99%
“…Data-based approaches have been widely applied to solve industrial and real-life problems and also studied in their theoretical aspects by the research community, which incudes data-based predictive control [23], unfalsified control [22], Markov data-based linear quadratic Gaussian control [11], disturbance-based control [24], simultaneous perturbation stochastic approximation [12], virtual reference feedback tuning [25], pulse response based control [26]. In [14], databased optimal control was investigated.…”
Section: Introductionmentioning
confidence: 99%