Adaptive Backstepping Neural Controller for Magnetic Bearing System

This chapter presents an adaptive Backstepping neural controller (ABNC) to achieve precise rotor position tracking for a nonlinear active magnetic bearing (AMB) system with modeling uncertainties and external disturbances. In the proposed ABNC, the FNNs are used to approximate the unknown nonlinearities of dynamic systems and then based on the approximated models, the neural controller is constructed. The hidden node parameters of the FNNs are determined using the ELM, where these parameters are assigned randomly without adjusting. This simplifies the controller design process. Using the Lyapunov theory, stable tuning rules are derived for the update of the output weights of the FNNs and a proof of stability in the uniformly bounded sense is given for the resulting controller.