Sliding mode control with neural network for active magnetic bearing system
Z. Cao (TU Delft - DC systems, Energy conversion & Storage, Southeast University)
J. Dong (TU Delft - DC systems, Energy conversion & Storage)
F.M. Wani (TU Delft - Transport Engineering and Logistics)
H Polinder (TU Delft - Transport Engineering and Logistics)
P. Bauera (TU Delft - DC systems, Energy conversion & Storage)
Fei Peng (Southeast University)
Yunkai Huang (Southeast University)
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Abstract
A novel controller design procedure is proposed for a 5-degree-of-freedom (DOF) active magnetic bearing (AMB) system, based on sliding mode control (SMC) and neural network (NN). The SMC is used to achieve high robustness and fast response while the NN can compensate unmodeled uncertainty and external disturbance by on-line tuning algorithm. The proposed controller is compared with the well-tuned PID controller by simulations. The simulation results show the superior performance of the proposed controller.