Position tracking control for chaotic permanent magnet synchronous motors via indirect adaptive neural approximation

Position tracking control for the chaotic permanent magnet synchronous motor drive system is addressed in this paper. Neural networks are used to approximate the nonlinearities and indirect adaptive backstepping technique is employed to construct controllers. The designed indirect adaptive neural controllers can suppress chaos in the permanent magnet synchronous motor and guarantee that the position tracking error converges to a small neighborhood of the origin. Compared with the classical backstepping method, the proposed neural controllers׳ structure is very simple. Simulation results illustrate its effectiveness.