论文标题
使用带有内存的两层滤波器体系结构对开关播客系统的在线自适应识别
Online Adaptive Identification of Switched Affine Systems Using a Two-Tier Filter Architecture with Memory
论文作者
论文摘要
这项工作提出了一种用于多输入多输出（MIMO）的在线自适应识别方法，并具有保证参数收敛性的开关仿射系统。使用了一个在线参数估计器的家族，配备了双层低通滤波器体系结构，以促进每个子系统的参数学习和识别。过滤器以预测误差的形式捕获有关未知参数的信息，该信息在参数估计算法中使用。所提出的方法的显着特征将其与以前的大多数结果区分开来是使用存储库，该存储库存储过滤器值并促进了子系统的活动和非活动阶段的参数学习。具体而言，从一个子系统的先前活动阶段中学的经验保留在记忆中，并在其随后的活动阶段和不活跃的阶段中掌握了参数学习。此外，引入了一个新的间歇性初始激发（IIE）概念，该概念将先前确定的初始激发（IE）条件扩展到开关系统框架。 IIE被证明足以确保开关系统参数的指数收敛性。
This work proposes an online adaptive identification method for multi-input multi-output (MIMO) switched affine systems with guaranteed parameter convergence. A family of online parameter estimators is used that is equipped with a dual-layer low pass filter architecture to facilitate parameter learning and identification of each subsystem. The filters capture information about the unknown parameters in the form of a prediction error which is used in the parameter estimation algorithm. A salient feature of the proposed method that distinguishes it from most previous results is the use of a memory bank that stores filter values and promotes parameter learning during both active and inactive phases of a subsystem. Specifically, the learnt experience from the previous active phase of a subsystem is retained in the memory and leveraged for parameter learning in its subsequent active and inactive phases. Further, a new notion of intermittent initial excitation (IIE) is introduced that extends the previously established initial excitation (IE) condition to the switched system framework. IIE is shown to be sufficient to ensure exponential convergence of the switched system parameters.
