A composite neural network-based adaptive sliding mode control method for reluctance actuator maglev system
Author:
Funder
National Natural Science Foundation of China
China Postdoctoral Science Foundation
Postdoctoral Research Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-08551-8.pdf
Reference42 articles.
1. Zhou H, Deng H, Duan J (2017) Hybrid fuzzy decoupling control for a precision maglev motion system. IEEE/ASME Trans Mechatron 23(1):389–401
2. Ramirez-Laboreo E, Roes MGL, Sagues C (2019) Hybrid dynamical model for reluctance actuators including saturation, hysteresis, and eddy currents. IEEE/ASME Trans Mechatron 24(3):1396–1406
3. Sun Y, Qiang H, Mei X, Teng Y (2018) Modified repetitive learning control with unidirectional control input for uncertain nonlinear systems. Neural Comput Appl 30:2003–2012
4. Noshadi A, Shi J, Lee WS, Shi P, Kalam A (2016) Optimal pid-type fuzzy logic controller for a multi-input multi-output active magnetic bearing system. Neural Comput Appl 27(7):2031–2046
5. de Jesús Rubio J, Lughofer E, Pieper J, Cruz P, Martinez DI, Ochoa G, Islas MA, Garcia E (2021) Adapting h-infinity controller for the desired reference tracking of the sphere position in the maglev process. Inf Sci 569:669–686
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