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

Senkui Lu,Xingcheng Wang

doi:10.1109/access.2019.2942958

Abstract

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.

Highlights

Fractional calculus has a history of about 300 years
Inspired by the literature above, this paper considers the tracking control problem for fractional-order input saturated permanent magnet synchronous motor (PMSM) with uncertain nonlinear functions and load disturbance
Remark 2: Compared with the command filtered backstepping methods designed in [24], [33], [34], this paper mainly focuses on fractional-order system

Summary

Introduction

Fractional calculus has a history of about 300 years. It has become an active field of study and received increasing attention in recent years [1]–[4]. Many real problems can be modeled due to the application of fractional calculus, such as fractional stochastic systems, diffusion processes, signal processing, control processing, etc. Various significant researches of fractional calculus have already been made [5]. A synchronization criteria combined with an impulsive gain is developed in [6] to address the global synchronization problem for fractional-order complex networks. In [7], an adaptive control scheme via backstepping is proposed for fractional-order time delayed systems

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Conclusion