论文标题
未知的分段常数参数识别，收敛速率
Unknown Piecewise Constant Parameters Identification with Exponential Rate of Convergence
论文作者
论文摘要
这项研究的范围是在有限的激发条件下鉴定线性回归方程的未知分段常数参数。与已知的方法相比，为了使计算负担降低，只有一个模型可以识别回归的所有切换状态，并在开发的过程中使用以下两倍的贡献。首先，我们根据众所周知的DREM方法提出了一种新的真正在线估计算法，以检测切换时间并通过可调节的检测延迟来保留时间警觉性。其次，尽管开关信号函数尚不清楚，但自适应定律还是得出的，该定律可提供回归参数到其真实值的全局指数收敛，以防回归器在两个连续参数开关之间的时间间隔内有限令人兴奋。在分析上证明了拟议的识别程序对外部干扰影响的鲁棒性。通过数值实验证明了其有效性，其中使用抽象回归和二阶植物模型。
The scope of this research is the identification of unknown piecewise constant parameters of linear regression equation under the finite excitation condition. Compared to the known methods, to make the computational burden lower, only one model to identify all switching states of the regression is used in the developed procedure with the following two-fold contribution. First of all, we propose a new truly online estimation algorithm based on a well-known DREM approach to detect switching time and preserve time alertness with adjustable detection delay. Secondly, despite the fact that a switching signal function is unknown, the adaptive law is derived that provides global exponential convergence of the regression parameters to their true values in case the regressor is finitely exciting somewhere inside the time interval between two consecutive parameters switches. The robustness of the proposed identification procedure to the influence of external disturbances is analytically proved. Its effectiveness is demonstrated via numerical experiments, in which both abstract regressions and a second-order plant model are used.
