Research Initiation Award: Development of a Systematic Approach to Uncertainty Modeling for Robust Control Design

研究启动奖：鲁棒控制设计不确定性建模系统方法的开发

基本信息

批准号：
9110254
负责人：
Gary Balas
金额：
$ 7万
依托单位：
University of Minnesota-Twin Cities
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
1991
资助国家：
美国
起止时间：
1991-08-15 至 1994-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=9110254&HistoricalAwards=false
关键词：
Research Initiation Award Development Systematic

项目摘要

The objective of this proposal is to develop a systematic approach to uncertainty modeling from experimental data for robust control. In the past decade, theoretical developments in robust control have lead to more rigorous and applicable design techniques which allow the explicit incorporation of plant uncertainty into the problem formulation ı1,2,3,4!. These developments provide a fresh approach that can be exploited to control complex systems and achieve performance specifications which were unthinkable several years ago. The key link currently missing between these theoretical developments and their application in industry is a systematic approach to the generation of performance and uncertainty descriptions. The importance of accurate characterizations of uncertainty descriptions was vividly shown in the design of controllers for an experimental flexible structure ı5!. Controllers synthesized for "accurate" descriptions of the plant uncertainty yielded significant improvements in system performance when compared with other less accurate uncertainty descriptions. The proposed research will develop a methodology for attributing the error between theoretical system model and experimental data to uncertainty and noise model in a linear fractional framework which is compatible with the structure value mu-methodology. These same techniques can also be employed to develop performance descriptions in for this framework. The mu-framework will then be used to analyze and synthesize complex system controllers with these new performance and uncertainty descriptions. The improved modeling of a physical system for the purpose of control design will provide a two fold benefit: improved robustness properties and closed-loop system performance of the controller.

该提案的目标是开发一种系统的方法，根据鲁棒控制的实验数据进行不确定性建模。在过去的十年中，鲁棒控制的理论发展带来了更严格和适用的设计技术，允许将工厂不确定性明确地纳入到系统中。问题表述 ı1,2,3,4!。这些发展提供了一种新的方法，可用于控制复杂系统并实现几年前无法想象的性能规格。这些理论发展及其在工业中的应用之间目前缺少的关键环节。是产生绩效和不确定性的系统方法不确定性描述的准确表征的重要性在实验灵活结构的控制器设计中得到了生动的体现！与其他不太准确的不确定性相比，为“准确”描述工厂不确定性而合成的控制器产生了显着的系统性能改进。所提出的研究将开发一种将理论系统模型和实验数据之间的误差归因于线性分数框架中的不确定性和噪声模型的方法，该方法与结构值 mu 方法兼容，也可以采用这些相同的技术。然后，mu-框架将用于分析和综合具有这些新性能和不确定性描述的复杂系统控制器，用于控制设计的改进的物理系统建模将提供两个方面。好处：提高了控制器的鲁棒性和闭环系统性能。