Research Initiation Award: Multiple Model Estimation With Variable Structure

本文讨论了一类重要的状态/参数估计自适应方案，即多模型估计。这些方案的特点是使用多个模型，特别适用于涉及结构和参数变化的问题。MM估计的应用可以在许多领域找到，例如故障检测/隔离，机动目标跟踪，空中交通管制和自适应滤波。本文将重点研究以下三种变结构方案的智能框架：主动模型集、模型集切换和自适应网格。本文还将开展模型集选择的理论工作，为实际的MM估计器的设计提供坚实的基础和有用的指导。在此基础上，建立了基于图论的MM估计理论框架。这将导致对模型集进化（适应）的系统处理以及对现有方案的更好理解。将所开发的方案应用于实际问题，并将其性能和计算与现有方案进行比较。＊＊＊

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. ***