Modeling, Identification and Validation of Uncertain Systems; Theory and Experiments

不确定系统的建模、识别和验证；

• 批准号: 0218226
• 负责人: Roy Smith
• 金额: $ 23.5万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-10-01 至 2006-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0218226&HistoricalAwards=false
• 关键词: Modeling; Identification; Validation; Uncertain; Systems

The objective of the proposed work is to develop an integrated suite of tools and approaches for the modeling of uncertain systems for feedback control design purposes. Our modeling framework is based upon robust control theory, which is supported by several sophisticated control design methods. These models are in effect set descriptions, where the sets are generated by unknown but bounded dynamic perturbations within the model. These models allow the designer to capture the variety of uncertain behaviors that a physical system will generate. Although this is a powerful and flexible modeling framework, the tools available for modeling physical systems are limited. We address thisarea in this proposal, with the ultimate goal being the development of a suite of modeling techniques and software tools for control system modeling.Our prior work on model validation for robust control models forms the basis for the work proposed here. This approach has led to tools which allow the designer to experimentally quantify the difference between a physical system and the closest member of a robust control model set. In the area of theoretical model development we propose extending our current model validation approaches to describe non-linear and parametrically varying systems. In the experimental application area, two systems will be studied: control of semiconductor growth rate and composition in a metalorganic vapor deposition (MOCVD) reactor; control of unstable rotating magnetic bearing systems. Both applications have a great deal of industrial relevance, as well as providing challenging testbeds for the theoretical work. In the algorithmic development work, we propose refining structured algorithms to handle the very large problems that arise when applying model validation methods to data drivenproblems.The strong experimental component has significant wider benefits. The MOCVD experimental system is a nontraditional area for control work and fosters interaction and the exchange of ideas between the control theory community and semiconductor and materials scientists. The experiments are also integrated into engineering course work at UCSB, and are currently used as demonstration examples in undergraduate control courses and graduate level design laboratories.

拟议工作的目的是开发一套集成的工具和方法，以建模不确定系统以进行反馈控制设计目的。我们的建模框架基于强大的控制理论，该理论由几种复杂的控制设计方法支持。这些模型实际上是集合描述，其中集合是由模型中未知但有界的动态扰动生成的。这些模型允许设计人员捕获物理系统将产生的各种不确定行为。尽管这是一个强大而灵活的建模框架，但可用于建模物理系统的工具受到限制。我们在此提案中解决了这方面的问题，最终的目标是开发一套建模技术和用于控制系统建模的软件工具。我们先前在模型验证稳健控制模型的模型验证的工作构成了此处建议的工作的基础。这种方法导致了工具，该工具使设计人员可以实验量化物理系统与强大控制模型集的最接近成员之间的差异。 在理论模型开发领域，我们建议扩展当前的模型验证方法来描述非线性和参数变化的系统。在实验应用区域中，将研究两个系统：控制金属蒸气沉积（MOCVD）反应器中半导体生长速率和组成；控制不稳定的旋转磁轴承系统。这两种应用都具有很大的工业相关性，并为理论工作提供了具有挑战性的测试床。在算法开发工作中，我们提出了精炼的结构化算法，以处理将模型验证方法应用于数据驱动问题时出现的很大问题。强的实验组件具有明显的更广泛的好处。 MOCVD实验系统是用于控制工作的非传统领域，并促进了控制理论社区与半导体和材料科学家之间的思想交流。这些实验还集成到UCSB的工程课程中，目前被用作本科控制课程和研究生水平设计实验室的演示示例。