Brief PaperApproximate I/O Feedback Linearization of Discrete-Time Non-Linear Systems via Virtual Input Direct Design☆
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VRFT-based digital controller design using a generalized second-order reference model
2020, Computers and Chemical EngineeringCitation Excerpt :Some other iterative methods for adaptive design were proposed subsequently by Safonov (1994, 1997) and Spall and Cristion (1998). In order to alleviate the shortcomings of the aforementioned iterative techniques, a non-iterative procedure for data-based controller design was developed (Savaresi, 1997; Savaresi and Guardabassi, 1998). Campi et al. (2000) reorganized the work of Savaresi and Guardabassi (1998) to come up with an improved method called virtual reference feedback tuning (VRFT).
A new VRFT approach for IMC-PID feedback-feedforward controller design based on robustness
2020, IFAC-PapersOnLineAsymptotically exact direct data-driven multivariable controller tuning
2015, IFAC-PapersOnLineDirect design of a velocity controller and load disturbance estimation for a self-balancing industrial manual manipulator
2012, MechatronicsCitation Excerpt :Since the new manipulator layout no longer has a physical transducer for load weight measurement, a load weight observer has to be designed, in order to obtain mass estimates instead of weight measures. The first problem has been solved resorting to a direct control design approach: the Virtual Reference Feedback Tuning (VRFT) strategy, which allows the tuning of a feedback controller without having a model of the plant [1,2]. The VRFT method gives a solution to the problem of designing a controller for a system on the basis of a single set of I/O data.
Model free adaptive control with data dropouts
2011, Expert Systems with ApplicationsCitation Excerpt :Since these methods do not require a plant and/or disturbance model in the design of the controller, optimistic modeling assumptions and conservative model-error bounds are omitted. Up to now, there exists a few model free control or data-driven control methods with different names, such as: model free adaptive control (MFAC) (Hou, 1994; Hou & Huang, 1997; Hou, Han, & Huang, 1998, in press), iterative learning control (ILC) (Arimoto, Kawamura, & Miyazaki, 1984; Ahn, Chen, & Moore, 2007a; Chen & Wen, 2003; Xu, 1997), iterative feedback tuning (IFT) (Hjalmarsson, 2002, 2005; Hjalmarsson, Gunnarsson, & Gevers, 1995, 1998), virtual reference feedback tuning (VRFT) (Campi & Savaresi, 2006; Campi, Lecchini, & Savaresi, 2000, 2002; Savaresi & Guardabassi, 1998), unfalsified control (UC) (Anderson & Dehghani, 2007; Helvoort & Jager, 2007; Safonov & Tsao, 1995, 1997), etc. MFAC is an attractive technique which has gained a large amount of interests in the recent years.
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This paper was not presented at any IFAC meeting. This paper was recommended for publication in revised from by Associate Editor Henk Nijmeijer under the direction of Editor Tamer Baz̧ar.