Finite-time adaptive neural network control for fractional-order chaotic PMSM via command filtered backstepping

A finite-time adaptive neural network position tracking control method is considered for the fractional-order chaotic permanent magnet synchronous motor (PMSM) via command filtered backstepping in this paper. Firstly, a neural network with a fractional-order parametric update law is utilized to cope with the nonlinear and unknown functions. Then the command filtered technique is introduced to address the repeated derivative problem in backstepping. In addition, a novel finite-time control method is proposed by employing the fractional-order terminal sliding manifolds, designing the error compensation mechanism and the new virtual control laws. The finite-time convergence of the tracking error can be guaranteed by the proposed controller. Finally, the designed control method is verified by simulation results.