论文标题
任何时间接近移动范围的估计：稳定与遗憾
Anytime Proximity Moving Horizon Estimation: Stability and Regret
论文作者
论文摘要
在本文中，我们介绍了受约束离散时间线性系统的移动视野估计的有效实施。我们提出了一种新颖的迭代方案，该方案采用基于基础优化算法的基于接近度的公式，并通过仅执行有限数量的优化迭代来减少计算工作，每次收到新的测量。我们概述了确保基础估计错误的全球指数稳定性的条件。还建立了遗憾上限的迭代计划的绩效保证。一个组合的结果表明，可以通过增加优化迭代的数量来保证，指数稳定性和透明性遗憾都可以较小。提出的估计器的稳定性和遗憾结果通过数值模拟展示。
In this paper, we address the efficient implementation of moving horizon state estimation of constrained discrete-time linear systems. We propose a novel iteration scheme which employs a proximity-based formulation of the underlying optimization algorithm and reduces computational effort by performing only a limited number of optimization iterations each time a new measurement is received. We outline conditions under which global exponential stability of the underlying estimation errors is ensured. Performance guarantees of the iteration scheme in terms of regret upper bounds are also established. A combined result shows that both exponential stability and a sublinear regret which can be rendered smaller by increasing the number of optimization iterations can be guaranteed. The stability and regret results of the proposed estimator are showcased through numerical simulations.
