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

当一个人或一个设备运行的环境相对稳定但不确定时，可以缓慢地收集信息并用于决策。然而，当关键决策必须随着环境变化而迅速做出时，情况就完全不同了。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时。在这种情况下，(一)有关情况的信息不完整；(二)不确定或不可靠的信息；(三)资源有限。拟议研究的目的是尽可能有效地利用给定的资源，以找到适当的应对办法。研究的中心思想是利用来自分布式来源的信息，并随时决定如何将这些信息组合起来进行决策。考虑了持续监控自身性能并调整其控制策略以提高性能的系统。对于时不变对象，当参数不确定性较小时，这两种方法都是稳定和鲁棒的。然而，近年来，在生物和医学、经济和金融等各种领域以及能源管理、飞机和汽车控制以及安全等各种工程问题中，出现了对分析工具的需求，以便在存在输入和输出扰动的情况下有效地对大的不确定性和快速变化的环境做出反应。在许多这样的应用中，人们已经发现经典的自适应算法会导致大的和振荡的(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。这些包括a)切换、b)切换和调谐、c)交互适应和d)第二级适应。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。第三种方法基于新的模型定位方法，而第四种方法将未知参数的搜索限制在凸域内。在复杂的问题中，可能发生的变化不容易归类。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.