Adaptive backstepping neural controller for nonlinear thrust active magnetic bearing system
Zhao-Xu Yang, Guang-She Zhao, Hai-Jun Rong · 2014
This paper presents an adaptive backstepping neural (ABN) controller to achieve precise position tracking on the axial direction for a nonlinear thrust active magnetic bearing (TAMB) system. The proposed controller is constructed based on the single-hidden layer feedforward network (SLFN) for approximating the unknown nonlinearities of dynamic systems. Different from the existing methods the parameters of the SLFNs are modifie using the recently proposed neural algorithm named extreme learning machine (ELM), where the parameters of the hidden nodes are assigned randomly without adjusting. This simplifie the controller design process. The output weights are updated based on the Lyapunov synthesis approach to guarantee the stability of the overall control system. Finally the simulation results demonstrate that better tracking performance is achieved by the ABN controller than that of the conventional backstepping controller.