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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.
Aiming at the control problem of a class of linear time-varying systems, a self-learning sliding mode control method based on the extended state observer is proposed. Firstly, two kinds of nonlinear smoothing functions are designed. Then two kinds of smoothing functions are respectively applied to the extended state observer and In order to further improve the adaptive control ability of the system, the steepest descent method is used to self-learning the gain parameters of the sliding mode controller. The simulation results show that the proposed control method not only has the advantages of fast response, High precision and can effectively solve the problem that is difficult to be solved by the existing theoretical methods and is an effective LTV system control method that does not depend on the controlled object model.