Virtual reference feedback tuning: a direct method for the design of feedback controllers

By Marco Claudio Campi, Andrea Lecchini-Visintini, S. Savaresi. at - Automatisierungstechnik, 2002

982 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/fca8f8020e611d7e7cb77211e68a40550538cc3d

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In Control and Systems Engineering

Citations in the corpus

Improving Convergence of Iterative Feedback Tuning (2009, methodology): “Virtual Reference Feedback Tuning [1] and Correlation-based Tuning [20] are two data driven controller tuning methods which typically outperform Iterative Feedback Tuning in convergence rate and hence require fewer plant experiments.”
Data-driven inversion-based control of nonlinear systems (2015, methodology): “Algorithm 2 (Design of K lin ) Initialization: choose the controller order n θ and a reference model M as explained in Campi et al. (2002).”
Formulas for Data-Driven Control: Stabilization, Optimality, and Robustness (2019, background): “rsis@rug.nl. P. Tesi is with DINFO, University of Florence, 50139 Firenze, Italy E-mail: pietro.tesi@unifi.it. control theory [6], iterative feedback tuning [7], and virtual reference feedback tuning [8]. This topic is now attracting more and more researchers, with problems ranging from PIDlike cont”
From Noisy Data to Feedback Controllers: Nonconservative Design via a Matrix S-Lemma (2020, methodology): “We mention, for instance, virtual reference feedback tuning [31] and methods based on Willems’ fundamental lemma [32] (see also [33]).”
A fictitious reference iterative tuning (FRIT) in the two-degree of freedom control scheme and its a (2005, methodology): “At first glance, our proposed method “FRIT” seems to be similar to “VRFT” proposed by Campi in (Campi et al., 2002) and (Campi et al., 2003) the sense that we require only one-shot experimental data and the fictitious or virtual signals are used for the parameter tuning in the offline.”
Data‐driven model reference control with asymptotically guaranteed stability (2011, methodology): “In the numerical example of Section 5.1, addition of the stability constraints to VRFT improves the closed-loop performance.”
On Model-Free Adaptive Control and Its Stability Analysis (2019, methodology): “The model-free adaptive predictive control with applications in the oxygen concentration control for syngas manufacturing industry and in three-tank liquid level control system [27], [28]; MFAC-based sliding mode control scheme with application in gas pressure process control and DC-motor current co”
From model-based control to data-driven control: Survey, classification and perspective (2013, background): “Proposition 1 in [25] shows that selecting the appropriate filter for virtual signals e and u could ensure the two criterions have the same minimizer.”