An iterative partition-based moving horizon estimator with coupled inequality constraints
An iterative partition-based moving horizon estimator with coupled inequality constraints
复制标题
具有耦合不等式约束的基于迭代分区的移动水平估计器
DOI：
10.1016/j.automatica.2015.08.016
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
W. Marquardt
中科院分区：
文献类型：
--
作者：
R. Schneider;Ralf Hannemann;W. Marquardt
针对由相互作用的子系统组成的大规模线性系统，提出了一种迭代的、基于分区的移动视界状态估计器。每个子系统估计自己的状态和扰动变量，同时考虑到从邻近子系统接收到的估计。与其他基于分区的移动地平线估计器相比，该方法具有两个独特的特点：它可以处理估计变量上的耦合不等式约束，并且其状态估计可以任意接近集中式移动地平线估计器的最优状态估计。通过数值算例验证了该方法的适用性和性能，并严格证明了该方法的收敛性和渐近稳定性。
We propose an iterative, partition-based moving horizon state estimator for large-scale linear systems that consist of interacting subsystems. Every subsystem estimates its own state and disturbance variables, taking into account the estimates received from neighboring subsystems. Compared to other partition-based moving horizon estimators, the proposed method has two unique features: it can handle coupled inequality constraints on the estimated variables and its state estimates come arbitrarily close to the optimal state estimates of a centralized moving horizon estimator. The applicability and performance of the proposed method are demonstrated on a numerical example and convergence and asymptotic stability are rigorously proven.