期刊论文

Self-learning Backstepping Sliding Mode Control with Disturbance Rejection for Permanent Magnet Synchronous Motor

中文

控制工程

1671-7848

2021

28

01

93-98

10.14107/j.cnki.kzgc.20180377

湖南省教育厅重点项目（17A006）；

本校为第一机构

针对永磁同步电机(PMSM)速度跟踪控制问题,设计了一种自学习滑模反步抗扰控制方法。该方法设计了基于类Sigmoid函数的改进型跟踪微分器(TD)用于对输入信号和虚拟控制信号进行滤波,避免了对信号反复解析求导产生的微分爆炸问题,并设计了扩张状态观测器(ESO)用于对外界负载扰动进行实时观测估计和补偿。为了削弱抖振和提高系统的自适应能力,利用非线性光滑函数设计滑模趋近律并使用最速下降法对增益参数进行自学习镇定。通过基于Lyapunov和Barbalat的理论分析验证了系统的稳定性,仿真结果证明了该控制方法的有效性,且其具有较高的控制精度和抗扰能力。

A self-learning backstepping sliding mode control method based on active disturbance rejection control technology is proposed for the problem of PMSM speed tracking control.An improved TD which based on the Sigmoid-like function is designed to filter the input signal and the virtual control signal.It can avoid the problem of differential signal explosion caused by resolution of signals repeatedly.Also an extended state observer (ESO) is designed to estimate external load disturbances in real time and compensate.In order to weaken the buffeting problem of the sliding model and improve the adapt...