Research Initiation Award: Multiple Model Estimation With Variable Structure

该建议涉及一类重要的用于状态/参数估计的自适应方案，称为多模型（MM）估计。 这些计划的特点是使用多个模型，特别是涉及结构和参数变化的问题。 MM估计在故障检测/隔离、机动目标跟踪、空中交通管制和自适应滤波等领域有着广泛的应用。 一个智能框架，以下三个变结构计划将特别开发：主动模型集，模型集切换，自适应网格。 本文还对模型集的选择进行了理论研究，为实际MM估计量的设计提供了坚实的基础和有用的指导。 此外，基于图论的MM估计的理论框架将被建立。 这将导致系统处理模型集的演变（适应），以及更好地了解现有的计划。 将所提出的格式应用于实际问题，并将其性能和计算量与现有格式进行比较。 ***

9409358 Li This proposal deals with an important class of adaptive schemes for state/parameter estimation, known as multiple model (MM) estimation. These schemes are characterized by using multiple models and are particularly good for problems involving both structural as well as parametrical changes. Applications of MM estimation can be found in many areas, such as failure detection/isolation, maneuvering target tracking, air traffic control, and adaptive filtering. An intelligent framework of the following three variable structure schemes will be developed in particular: active model set, model-set switching, and adaptive grid. Theoretical work on model set selection will also be carried out, which will provide a solid basis and a useful guideline for design of practical MM estimators. Moreover, a theoretical framework for the MM estimation will be established based on graph theory. This will lead to systematic treatment of model set evolution (adaptation) as well as better understanding of the existing schemes. The developed schemes will be applied to practical problems and their performance and computation will be compared with those of the existing schemes. ***