Command Filtering-Based Neural Network Control for Fractional-Order PMSM With Input Saturation

Command filtering-based neural network control is investigated in this paper for fractional-order input saturated permanent magnet synchronous motor (PMSM). First, the fractional-order command filter is introduced to cope with the “explosion of complexity” problem caused by the repeated derivatives of virtual signals in backstepping. Next, a compensation mechanism related to error is investigated to decrease the filtering errors under fractional calculus framework. Then, a neural network with its weight being updated online is accepted to eliminate restrictions on the uncertain nonlinear functions. Besides, the minimal learning parameterization technique is introduced to construct fractional-order adaptive law for the parameters of the neural network. Finally, the simulation results testify the availability and advantage of the designed approach.