论文标题
关于实施自适应和过滤的MHE
On the implementation of Adaptive and Filtered MHE
论文作者
论文摘要
多年来，已经开发了基于优化的算法（MHE）。本文说明了提交给IFAC第18届IFAC COMPATION研讨会上有关优化应用程序[Oliva和Carnevale，2022年]的政策的实施，我们提出了两种技术来降低MES的计算成本。这些解决方案主要依赖于输出过滤和自适应采样。过滤器的使用减少了MHE使用的数据总量，缩短了移动窗口的长度（缓冲区），从而减少了植物动力学集成的时间消耗。同时，提出的自适应抽样策略丢弃了那些不允许明智改善估计误差的采样数据。提供算法和数值模拟，以显示拟议策略的有效性。
Optimisation-based algorithms known as Moving Horizon Estimator (MHE) have been developed through the years. This paper illustrates the implementation of the policy introduced in the companion paper submitted to the 18th IFAC Workshop on Control Applications of Optimization [Oliva and Carnevale, 2022], in which we propose two techniques to reduce the computational cost of MHEs. These solutions mainly rely on output filtering and adaptive sampling. The use of filters reduces the total amount of data used by MHE, shortening the length of the moving window (buffer) and consequently decreasing the time consumption for plant dynamics integration. Meanwhile, the proposed adaptive sampling policy discards those sampled data that do not allow a sensible improvement of the estimation error. Algorithms and numerical simulations are provided to show the effectiveness of the proposed strategies.
