How to adapt efficiently using distributed resources and multiple models to time varing dynamic systems

如何使用分布式资源和多个模型有效地适应时变动态系统

• 批准号: 1503751
• 负责人: Kumpati Narendra
• 金额: $ 35.88万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1503751&HistoricalAwards=false
• 关键词: How; adapt; efficiently; using; distributed

When the environment in which a person or a device is operating is relatively constant, but uncertain, information can be collected slowly and used to make decisions. However, the situation is quite different when critical decisions have to be made rapidly as the environment changes. Such situations arise in medical emergencies, trading in the stock market, conflict management, and taking counter-terror measures. In such cases, there is (i) incomplete information about the situation (ii) uncertain or unreliable information and (iii) limited resources. The aim of the proposed research is to use the given resources as efficiently as possible to find adequate responses. The central idea of the research is to exploit the information coming from distributed sources, and decide at every instant how to combine the information for decision making. Systems that continuously monitor their own performance, and adjust their control strategies to improve the performance are considered. These are both stable and robust for time-invariant plants when the parametric uncertainty is small. However, in recent years the need for analytic tools for reacting effectively to large uncertainties and rapidly varying environments, in the presence of input and output disturbances, has been arising in a variety of fields including biology and medicine, economics and finance, and various engineering problems such as energy management, aircraft and automotive control, and security. In many of these applications, it has been found that classical adaptive algorithms result in large and oscillatory (and possibly unstable) responses. To address such problems, the use of different methods based on multiple models has been proposed by the PI. These include a) Switching b) Switching and Tuning c) Interactive adaptation and d) Second Level adaptation. The first method involves switching between fixed models, while the second method combines adaptive switching and continuous adaptation. The third method is based on novel ways of locating models, while the fourth method confined search of unknown parameters to convex regions. In complex problems, the variations that can occur cannot be classified easily. The proposal attempts to study methods for adapting efficiently by distributing resources.

当一个人或一个设备的操作环境相对恒定但不确定时，可以缓慢收集信息并用于决策。然而，当环境发生变化而必须迅速做出关键决策时，情况就大不相同了。这种情况出现在医疗紧急情况、股票市场交易、冲突管理和采取反恐措施中。在这种情况下，（一）有关情况的信息不完整;（二）信息不确定或不可靠;（三）资源有限。拟议研究的目的是尽可能有效地利用现有资源，找到适当的对策。研究的中心思想是充分利用来自分布式资源的信息，并在每一时刻决定如何将这些信息联合收割机组合起来用于决策。系统不断监测自己的性能，并调整其控制策略，以提高性能。当参数不确定性很小时，这些对于时不变对象是稳定的和鲁棒的。然而，近年来，在各种领域，包括生物学和医学，经济学和金融，以及各种工程问题，如能源管理，飞机和汽车控制，以及安全，在输入和输出干扰的存在下，对分析工具的需求已经出现，以有效地应对大的不确定性和快速变化的环境。在许多这些应用中，已经发现经典的自适应算法导致大的和振荡的（并且可能不稳定的）响应。为了解决这些问题，PI提出了使用基于多个模型的不同方法。其中包括：（1）转换 B）切换和调谐，c）交互式适配和d）第二级适配。第一种方法涉及在固定模型之间切换，而第二种方法结合了自适应切换和连续自适应。第三种方法是基于新的方法定位模型，而第四种方法限制搜索未知参数的凸区域。在复杂的问题中，可能发生的变化无法轻易分类。该提案试图研究通过分配资源来有效适应的方法。