基于ESO的一类线性时变系统自学习滑模控制方法  

Self-learning sliding mode control method of a class of linear time-varying systems based on ESO

作　　者：曾喆昭[1] 吴亮东[1] 陈韦名 

机构地区：[1]长沙理工大学电气与信息工程学院,长沙410076

出　　处：《控制与决策》2016年第11期2101-2105,共5页Control and Decision

基　　金：国家自然科学基金项目(61040049);电子科学与技术湖南省重点学科和智能电网运行与控制湖南省重点实验室项目

摘　　要：针对一类线性时变系统的控制问题,提出一种基于扩张状态观测器的自学习滑模控制方法.首先设计两种非线性光滑函数;然后将两种光滑函数分别应用于扩张状态观测器和滑模趋近律的设计.为了进一步提高系统的自适应控制能力,使用最速下降法对滑模控制器的增益参数进行自学习镇定.仿真结果表明,所提出的控制方法不仅响应速度快、控制精度高,而且能够有效解决现有理论方法难以解决的问题,是一种有效的不依赖于被控对象模型的LTV系统控制方法.A self-learning sliding mode control（SLSMC） method is proposed for a class of linear time-varying（LTV）systems based on extended state observer（ESO）. Firstly, two kinds of nonlinear smooth functions（NSF） are designed, and then two smooth functions are applied to the design of the extended state observer and the sliding mode reaching law. In order to further improve the adaptive control ability of linear time-varying systems, the steepest descent method is used to update gain parameters of the sliding mode controller through the self-learning algorithm. Simulation results show that the proposed control method not only has fast response and high control precision, but also can effectively solve the problem that the existing theoretical methods are difficult to solve, hence it is an effective control method independent of the model of the controlled LTV systems.

关 键 词：线性时变系统 滑模控制 扩张状态观测器 非线性光滑函数 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]