2020
DOI: 10.1109/tte.2020.3006722
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Data-Driven Model-Free Adaptive Current Control of a Wound Rotor Synchronous Machine Drive System

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Cited by 36 publications
(11 citation statements)
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“…From the energy point of view, Assumption 2 means that a physical plant's energy change rate is limited if the control input changes within a finite altitude. Many practical systems, such as the wound rotor synchronous machine system, 38 variable polarity plasma arc welding system, 39 and even for implantable heart pump system, 40 have been proved to satisfy this assumption.…”
Section: Problem Formulationmentioning
confidence: 99%
“…From the energy point of view, Assumption 2 means that a physical plant's energy change rate is limited if the control input changes within a finite altitude. Many practical systems, such as the wound rotor synchronous machine system, 38 variable polarity plasma arc welding system, 39 and even for implantable heart pump system, 40 have been proved to satisfy this assumption.…”
Section: Problem Formulationmentioning
confidence: 99%
“…To ensure the control precision, data-driven control can be employed for the presynchronization control of ship microgrid inverters. One such method is model-free adaptive control (MFAC), which is extensively utilized in the control of wound rotor synchronous machines in wound rotor synchronous machine control (Hashjin et al, 2020), wind farm control (Shi et al, 2020), aircraft trajectory control (Jiang et al, 2021), and other fields in recent years. MFAC creates a dynamic linearization method by utilizing the system inputs and outputs (Xiong and Hou, 2020), which is suitable for presynchronization controller design with the following reasons: (1) accurate mathematical model of controlled system and external testing signals are not required; (2) the MFAC controller has small calculation burden, simple structure, finite-time convergence, and predominant robustness; and (3) the stability of MFAC controller can be guaranteed under reasonable assumptions.…”
Section: Introductionmentioning
confidence: 99%
“…A lazy‐learning‐based predictive MFAC 14 is proposed for nonlinear systems. Additionally, MFAC has found many successful applications, including machine drive systems, 15 chemical process control, 16 wind turbine control systems, 17 multi‐agent systems 18 and so forth.…”
Section: Introductionmentioning
confidence: 99%