Modeling and Characterization of a Flexible Rotor Supported by Ac- tive Magnetic Bearings Using Model Reduction Techniques

Carvalho, Felipe Carmo; Oliveira, Marcus Vinícius Fernandes de; Pereira, Bruno; Cavalini, Aldemir Ap; Steffen, Valder

doi:10.26678/abcm.diname2019.din2019-0208

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“…where k 0 is the mean value of the gain, δ k 0 is the dispersion level, ξ(θ) represents a Gaussian unit random variable, and θ is a random process. The definition of the uncertain gains of the filters was obtained based on the procedure presented in Carvalho et al (2019). Therefore, the uncertain gains were defined according to equation ( 13) for the DE and NDE of the AMBs; thus, k 0 DE ¼ 0:275, δ k 0 DE ¼ 0:45, k 0 NDE ¼ 1:45, and δ k 0 NDE ¼ 0:38.…”

Section: Robust Optimal Design Of the Neuro-fuzzy Controllermentioning

confidence: 99%

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Fuzzy robust control applied to rotor supported by active magnetic bearing

Carvalho

Oliveira

Lara-Molina

et al. 2020

Journal of Vibration and Control

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The technology associated with active magnetic bearings has been widely used in the last years and can be considered as being one of the most promising solutions for several applications in rotating machinery. Lubricants are not necessary, and high rotation speeds are reached without any relevant heating. Active magnetic bearings are classified as mechatronic systems because they are composed of mechanical and electronic parts that are controlled by using dedicated software. In this context, the present work is devoted to the design of robust controllers applied to supercritical rotors supported by active magnetic bearings. For this aim, numerical and experimental tests were carried out. Different from previous studies reported in the literature, the present contribution proposes a novel design procedure to robustify the neuro-fuzzy controller of a rotor supported by active magnetic bearings based on optimal robust design. This optimal design procedure tunes the robust neuro-fuzzy controller taking into account the optimal balance between vibration attenuation performance and robustness, that is the increase in vibration attenuation implies the reduction in the robustness. The first stage of the controller synthesis is dedicated to the specification of all design requirements. Then, the adaptative neuro-fuzzy controller was obtained, starting from the determination of the plant dominant poles and finally performing the model-based analysis of the system stability and performance. Finally, the vibration control performance and robustness are optimally balanced by using a robust optimization procedure. The behavior of the controller was evaluated by investigating the unbalance response of the rotating system. The obtained results demonstrated the effectiveness of the conveyed approach.

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Section: Robust Optimal Design Of the Neuro-fuzzy Controllermentioning

confidence: 99%

“…The definition of the uncertain gains of the filters was obtained based on the procedure presented in Carvalho et al (2019). Therefore, the uncertain gains were defined according to equation (13) for the DE and NDE of the AMBs; thus, k0DE=0.275, δk0DE=0.45, k0NDE=1.45, and δk0NDE=0.38.…”

Section: Robust Optimal Design Of the Neuro-fuzzy Controllermentioning

confidence: 99%