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

当人或设备运行的环境相对恒定但不确定时，可以缓慢地收集信息并用于做出决策。然而，当环境变化而必须迅速做出关键决策时，情况就大不相同了。这种情况会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，存在 (i) 有关情况的信息不完整 (ii) 信息不确定或不可靠以及 (iii) 资源有限。拟议研究的目的是尽可能有效地利用给定资源来找到适当的应对措施。该研究的中心思想是利用来自分布式来源的信息，并在每个时刻决定如何组合这些信息进行决策。考虑持续监控自身性能并调整控制策略以提高性能的系统。当参数不确定性很小时，这些对于时不变工厂来说既稳定又鲁棒。然而，近年来，在生物和医学、经济和金融以及各种工程问题（例如能源管理、飞机和汽车控制以及安全）等领域，越来越需要在存在输入和输出干扰的情况下对巨大的不确定性和快速变化的环境做出有效反应的分析工具。在许多这样的应用中，人们发现经典的自适应算法会导致大的振荡（并且可能不稳定）响应。为了解决此类问题，PI 提出了使用基于多个模型的不同方法。其中包括 a) 切换 b) 切换和调整 c) 交互式适应和 d) 二级适应。第一种方法涉及固定模型之间的切换，而第二种方法则结合了自适应切换和持续自适应。第三种方法基于定位模型的新颖方法，而第四种方法将未知参数的搜索限制在凸区域。在复杂问题中，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。

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.