Robust adaptive neural network tracking control of a permanent magnet spherical motor

Considering the influence of the unknown uncertainties, a robust adaptive neural network control approach is employed to the controller design for permanent magnet spherical motor (PMSM) under the continuous tracking mode. The unknown nonlinear model can be approximately learning by RBF neural network (RBFNN) system. With the aid of robust items, the external disturbance problem and the approximation errors are solved. It is proved that the proposed adaptive control scheme can guarantee the PMSM rotor dynamic system stability based on Lyapunov analysis. Simulation studies show the effectiveness of the proposed approach. Also, the results in this paper could serve as a basis for the future research and experiment.