论文标题
强大的在线联合状态/输入/参数估计线性系统
Robust online joint state/input/parameter estimation of linear systems
论文作者
论文摘要
本文提出了一种以在线方式共同估计线性系统的状态，输入和参数的方法。该方法是专门设计用于用非高斯噪声或离群值损坏的测量值的，这些噪声或离群值通常在工程应用中发现。特别是，它将递归，交替和迭代的最小二乘正方形结合到一个单步算法中，该算法在线解决了估计问题，并带来了最小二差回归方法的鲁棒性。迭代方法的收敛是正式保证的。数值实验表明，与最新方法相比，在存在异常值的情况下，估计算法的良好性能。
This paper presents a method for jointly estimating the state, input, and parameters of linear systems in an online fashion. The method is specially designed for measurements that are corrupted with non-Gaussian noise or outliers, which are commonly found in engineering applications. In particular, it combines recursive, alternating, and iteratively-reweighted least squares into a single, one-step algorithm, which solves the estimation problem online and benefits from the robustness of least-deviation regression methods. The convergence of the iterative method is formally guaranteed. Numerical experiments show the good performance of the estimation algorithm in presence of outliers and in comparison to state-of-the-art methods.
