Adaptive neural network control for rotor's stable suspension of Vernier-gimballing magnetically suspended flywheel

Vernier-gimballing magnetically suspended flywheel with conical hybrid magnetic bearing can produce gyro moment by tilting the rotational rotor around a certain radial direction. When rotor is tilted, the nonlinear variation of conical magnetic bearing's displacement stiffness and the coupling interference can result in not only poor stability of suspension control system but also precision's degradation of gyro moment. To solve these two problems, the forces acting on tilted rotor are analyzed, and furthermore, a nonlinear model considering variation of displacement stiffness is constructed accordingly, then a radial basis function neural network is utilized to estimate the nonlinear variation of displacement stiffness and disturbance, and the adaptive controller with nonlinear variation compensation is presented based on the Lyapunov stability theory. Compared with cross-feedback method in tilting control and proportional-integral-derivative method in translation control, simulation researches are done, and the results indicate that the presented control method does well in estimating nonlinear variation and has better performances on suspension control when rotor is tilted.