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

被引：14

作者：

Lu, Senkui ^{[1
]}

Wang, Xingcheng ^{[1
]}

Wang, Longda ^{[1
]}

机构：

[1] Dalian Maritime Univ, Coll Marine Elect Engn, Dalian, Peoples R China

来源：

ADVANCES IN DIFFERENCE EQUATIONS | 2020年 / 2020卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Fractional-order PMSM; Finite-time convergence; Command filter; Adaptive neural network; SLIDING MODE CONTROL; MAGNET SYNCHRONOUS MOTORS; TRACKING CONTROL; TORSIONAL VIBRATION; FEEDBACK-CONTROL; OUTPUT-FEEDBACK; SYNCHRONIZATION; STABILIZATION; SYSTEM; BIFURCATION;

D O I：

10.1186/s13662-020-02572-6

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

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.

引用

页数：21

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