论文标题
使用递归最小二乘
Adaptive Extremum Seeking Using Recursive Least Squares
论文作者
论文摘要
由于其基于非模型的分析和实施，Extremum寻求（ES）优化方法非常受欢迎。这种方法主要用于基于梯度的搜索算法。由于通常观察到最小二乘（LS）算法是优越的，因此在收敛速度和对测量噪声的稳健性方面，高于梯度算法，因此预计基于LS的ES方案还将为传感器提供更快的收敛性和稳健性。在本文中，通过这种动机，设计和分析了基于递归的最小二乘（RLS）估计的ES方案，以应用于标量参数和矢量参数静态图和动态系统。在所有情况下，都建立了渐近的趋势融合到极值。提供了仿真研究以验证拟议方案的性能。
Extremum seeking (ES) optimization approach has been very popular due to its non-model based analysis and implementation. This approach has been mostly used with gradient based search algorithms. Since least squares (LS) algorithms are typically observed to be superior, in terms of convergence speed and robustness to measurement noises, over gradient algorithms, it is expected that LS based ES schemes will also provide faster convergence and robustness to sensor noises. In this paper, with this motivation, a recursive least squares (RLS) estimation based ES scheme is designed and analysed for application to scalar parameter and vector parameter static map and dynamic systems. Asymptotic convergence to the extremum is established for all the cases. Simulation studies are provided to validate the performance of proposed scheme.
