最小方差约束估计器
Minimum variance constrained estimator
AI总结：
本文提出了一种基于最小方差对偶性的稳定估计器，用于解决离散时间线性系统中存在状态约束的状态估计问题，并通过数值实验与传统方法进行比较。
摘要：
本文关注在存在额外（等式或不等式）状态（或估计）约束的情况下，离散时间线性系统的状态估计问题。通过使用最小方差对偶性，将估计问题转换为最优控制问题。描述了两种算法解决方案：全信息估计器（FIE）和移动时间窗估计器（MHE）。主要结果是证明所提出的估计器在观测器意义上是稳定的。所提出的算法与基于最小能量对偶性的标准约束状态估计算法不同。在基准釜式反应器过程模型上进行了数值比较。
英文摘要：
This paper is concerned with the problem of state estimation for discrete-time linear systems in the presence of additional (equality or inequality) constraints on the state (or estimate). By use of the minimum variance duality, the estimation problem is converted into an optimal control problem. Two algorithmic solutions are described: the full information estimator (FIE) and the moving horizon estimator (MHE). The main result is to show that the proposed estimator is stable in the sense of an observer. The proposed algorithm is distinct from the standard algorithm for constrained state estimation based upon the use of the minimum energy duality. The two are compared numerically on the benchmark batch reactor process model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
