Decentralized optimal control of cooperating networked multi-agent systems

协作网络多智能体系统的分散最优控制

基本信息

批准号：
1931600
负责人：
Sean Andersson
金额：
$ 39.99万
依托单位：
Trustees of Boston University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931600&HistoricalAwards=false
关键词：
Decentralized optimal control cooperating networked

项目摘要

Multi-agent systems encompass a broad spectrum of applications, ranging from connected autonomous vehicles and the emerging internet of cars, where the spatial domain may be hundreds of miles with time horizons over hours of days, to micro-air vehicles which operate over meter length and minute time scales, and down to nano-manipulation with nanometer spatial microsecond time resolution. This project seeks to address five key challenges in networked multi-agent systems: (1) scalability, necessitated by the increasing large-scale nature of the networked systems being designed, (2) autonomy at the individual level, required to ensure a resilient and secure system, (3) communication that is secure and efficient, particularly crucial in wireless settings where the agents have limited energy resources, (4) avoiding local optima that arise from the complex nature of the system interactions and which may yield poor performance, and (5) exploiting real-time data, taking advantage of the modern reality of data-rich environments. While the core of the project is centered on a theoretical approach that extends over the diverse length and time scales needed, it also includes experimental validation using robotic platforms that will provide a platform to showcase and communicate results to a broad audience.The scope of the proposed project is captured through a general optimization (both static and dynamic) framework which encompasses the vast majority of interesting problems faced by researchers and practitioners. Within this framework, we will pursue three specific tasks: (1) Develop on-line solutions for dynamic optimization problems in networked multi-agent systems, (2) Determine when decentralization without sacrificing the performance of a centralized solution is possible and develop explicit decentralized control algorithms even in cases where some performance degradation is needed, and (3) Address the challenge of multiple local minima in the optimization through the use of boosting functions to escape those local optima. The intellectual merit of these tasks lies in three conceptual cornerstones: (1) Replacing the traditional time- driven paradigm with an event-driven approach, allowing for algorithms whose complexity grows with the number of such events, not the state dimensionality of the network, (2) Using a data-driven approach to optimization, allowing for an approach which can handle the increasing complexity of real-world systems where traditional approaches based on elegant but often inadequate classical models fail, and (3) Escaping local optima in distributed optimization, where the use of novel mechanisms for escaping those local solutions overcomes the limitations inherent to gradient-based approaches. The project is built upon a framework for networked multi-agent systems that is extremely broad, encompassing sub-problems such as coverage control, consensus, persistent monitoring, and optimal formation control, and application domains from connected automated vehicles down to nano-manipulation. As such, our research will advance the state-of-the-art in all domains that rely on networked systems. In addition, specific tasks on education and outreach will be pursued, including hosting rising high school seniors in the labs of the PIs for a summer research internship, showcasing the results to middle and high-school students through demonstrations with mobile robots, and engaging undergraduate students in research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

多机构系统涵盖了广泛的应用程序，从连接的自动驾驶汽车和新兴的汽车互联网范围内，空间域可能会在几个小时内随时间范围内随时间范围而到数百英里，再到微型航空车辆，这些汽车的微型机车，这些车辆可以通过米量和微小的时间尺度进行操作，再到Nano-Nano-Manifulation tos Nano-nano-Memepation-Nano-Anipulation-Nano-Anipulation-Nano-Anaino-AnanoMecondial Spatial Spatidial SpatiDolution。 This project seeks to address five key challenges in networked multi-agent systems: (1) scalability, necessitated by the increasing large-scale nature of the networked systems being designed, (2) autonomy at the individual level, required to ensure a resilient and secure system, (3) communication that is secure and efficient, particularly crucial in wireless settings where the agents have limited energy resources, (4) avoiding local optima that arise from the complex nature of the system interactions and which可能会产生差的性能，（5）利用数据丰富的环境的现代现实来利用实时数据。虽然该项目的核心集中在一种理论方法上，该方法扩展了所需的多样性和时间尺度，但它还包括使用机器人平台的实验验证，该验证将提供一个平台，以向广泛的受众群体展示和传达结果。通过一般性优化（静态和动态）范围捕获了拟议项目的范围，这些框架涵盖了众多的研究者和研究者，由研究人员置于研究者和练习者。在此框架内，我们将追求三个特定的任务：（1）开发在网络多代理系统中动态优化问题的在线解决方案，（2）在不牺牲集中式解决方案的性能的情况下何时进行分散式化的效果，并开发出明确的分散性控制算法，即使在需要偏离的情况下，即使在多个位置质疑的情况下，也需要（3）启动的功能，并且（3）在多个位置的范围内（3），以及（3），以及3） Optima。这些任务的智力优点在于三个概念基石：（1）用事件驱动的方法替换传统的时间驱动范式，允许其复杂性随着此类事件的数量而增长的算法，而不是通过网络的状态维度来实现的，（2）使用越来越多的数据进行优化的方法，可以在范围内进行优化的方法，从而可以换取一种方法，从而可以进行优化的方法，从而可以努力地进行优化，从而可以努力地努力，从而可以努力地进行优化，从而可以努力地努力。不足的经典模型失败，（3）在分布式优化中逃脱了局部优化，其中使用新型机制逃脱了这些局部解决方案克服了基于梯度的方法固有的局限性。该项目建立在一个网络多代理系统的框架基础上，该系统非常广泛，包括覆盖范围控制，共识，持续性监控和最佳的形成控制以及从连接的自动化车辆到纳米操作的应用域。因此，我们的研究将推进依赖网络系统的所有领域的最新面积。此外，还将进行有关教育和宣传的具体任务，包括在PI的实验室中举办高中生，以进行夏季研究实习，向中学和高中生展示结果，并通过移动机器人进行演示，并让本科生参与研究中的研究。