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• W2151172634 abstract "A novel approach is presented that couples the hard computing features of robust H/sub /spl infin// control with the soft computing characteristics of system identification, and realizes the benefits of both. Radial basis function networks (RBFNs) are used to experimentally identify linear parameter varying (LPV) dynamic models and uncertainty models associated with an active magnetic bearing (AMB) test rig. These models are used to synthesize robust H/sub /spl infin// controllers that are gain-scheduled with rotor setpoint. Experimental results confirm the stability robustness and exceptional tracking performance of these intelligent robust controllers." @default.
• W2151172634 created "2016-06-24" @default.
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• W2151172634 date "2004-04-23" @default.
• W2151172634 modified "2023-09-25" @default.
• W2151172634 title "H/sub ?/ control of active magnetic bearings using artificial neural network identification of uncertainty" @default.
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• W2151172634 doi "https://doi.org/10.1109/icsmc.2003.1244616" @default.
• W2151172634 hasPublicationYear "2004" @default.
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