Towards Computationally Efficient One-Shot Design for Performance-Critical Distributed Multi-Agent Control

面向性能关键的分布式多智能体控制的计算高效的一次性设计

• 批准号: 1952862
• 负责人: Chengzhi Yuan
• 金额: $ 41.32万
• 依托单位: University of Rhode Island
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1952862&HistoricalAwards=false
• 关键词: Towards; Computationally; Efficient; Shot; Design

Multi-agent systems, an important class of interconnected/networked systems composing a group of distributed interacting entities, have been emerging as a powerful paradigm for various unprecedented engineering applications, such as spacecraft formation flying, air-traffic management, sensory networks, etc. Through collaboration, a multi-agent system can accomplish numerous complicated control tasks that surpass the capability of a single dynamical system, such as moving an oversized object, environmental monitoring, and disaster search/rescue. Moreover, a multi-agent system can solve some problems faster using parallelism and increase robustness through redundancy. However, implementing cooperative multi-agent systems also presents challenges, making many related applications (especially those demanding critical controlled performance) remain conceptual. One important challenge lies in the lack of a systematic approach that allows control engineers to treat associated multi-agent distributed control design in a computationally-efficient and fully-integrated manner. Existing approaches of separating the design into high- and low-level controls often fail to analytically guarantee reliability, which is a critical requirement for acceptance by control engineers. This project supports fundamental research to provide the knowledge needed to overcome these challenges, thereby promoting broader real-world applications of multi-agent distributed control techniques. This project will also create unique opportunities to promote engineering education through the development of a cross-departmental robotics engineering program, and to boost minority involvement in scientific research.The goal of this project is to make fundamental contributions to the advancement of distributed multi-agent control theory by (i) developing novel hybrid switching control schemes to address complicated factors (e.g., agent’s physical dynamics, actuation and data sampling limitations, communication delays) in a holistic, one-shot distributed control design, and (ii) generating effective computational tools from combined deterministic and probabilistic perspectives to enable balancing design complexity and controlled performance. It will introduce innovative methodologies and tools to the field, leading to the following important paradigm changes: (i) from separated two-step designs dominantly adopted in current study to holistic, one-shot designs with provable network stability and controlled performance; (ii) from Lyapunov functions with simple quadratic forms dominantly utilized for current stability analysis and distributed control synthesis to Lyapunov functions with advanced composite forms that would significantly reduce analysis conservatism and improve controlled performance; and (iii) from distributed algorithms with trivial state/output feedback controller structures dominantly exploited by existing methods to distributed algorithms with novel hybrid controller structures of mixed continuous-time and discrete-event dynamics, facilitating simplified distributed optimal control synthesis via off-line convex optimization.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.

多智能体系统是一类重要的互联/联网系统，由一组分布的交互实体组成，已成为航天器编队飞行、空中交通管理、传感网络等各种前所未有的工程应用的有力范例。通过协作，多智能体系统可以完成许多复杂的控制任务，这些控制任务超过了单一动力系统的能力，如移动超大物体、环境监测和灾害搜索/救援。此外，多智能体系统可以利用并行性更快地解决一些问题，并通过冗余增加健壮性。然而，实现协作的多智能体系统也带来了挑战，使得许多相关的应用(特别是那些要求关键控制性能的应用)仍然停留在概念上。一个重要的挑战在于缺乏一种系统的方法，使控制工程师能够以计算高效和完全集成的方式处理相关的多代理分布式控制设计。现有的将设计划分为高级别和低级别控制的方法往往无法从分析上保证可靠性，而可靠性是控制工程师接受的关键要求。该项目支持基础研究，以提供克服这些挑战所需的知识，从而促进多代理分布式控制技术在现实世界中更广泛的应用。该项目还将创造独特的机会，通过开发跨部门的机器人工程计划来促进工程教育，并促进少数人参与科学研究。该项目的目标是通过(I)开发新颖的混合切换控制方案来解决整体、一次性分布式控制设计中的复杂因素(例如，智能体的物理动力学、驱动和数据采样限制、通信延迟)，以及(Ii)从确定性和概率的组合角度生成有效的计算工具，以平衡设计复杂性和受控性能，从而为分布式多智能体控制理论的发展做出基础性贡献。它将把创新的方法和工具引入该领域，导致以下重要的范式变化：(I)从当前研究中主要采用的分离的两步设计，到具有可证明的网络稳定性和受控性能的整体、一次性设计；(Ii)从主要用于当前稳定性分析和分布式控制综合的简单二次型的李亚普诺夫函数，到具有高级复合形式的李亚普诺夫函数，这将显著降低分析保守性并改善受控性能；以及(Iii)从现有方法主要利用的简单状态/输出反馈控制器结构的分布式算法，到具有混合连续时间和离散事件动态的新型混合控制器结构的分布式算法，通过离线凸优化促进简化的分布式最优控制综合。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A PHD Filter Based Localization System for Robotic Swarms

• DOI: 10.1007/978-3-030-92790-5_14
• 发表时间: 2021
• 作者: R. T. Perera;C. Yuan;P. Stegagno

Hybrid control of switched LFT uncertain systems with time-varying input delays

• DOI: 10.1080/00207179.2021.1975197
• 发表时间: 2021-09
• 期刊: International Journal of Control
• 影响因子: 2.1
• 作者: C. Yuan;Yan Gu;Weizhen Zeng

Cooperative place recognition in robotic swarms

机器人群中的合作位置识别

• DOI: 10.1145/3412841.3441954
• 发表时间: 2021
• 期刊: Proceedings of the 36th Annual ACM Symposium on Applied Computing
• 作者: Brent, Sarah;Yuan, Chengzhi;Stegagno, Paolo
• 通讯作者: Stegagno, Paolo

Application of Distributed Linear Multi-Agent Containment Control to Robotic Systems

• DOI: 10.1016/j.ifacol.2022.11.240
• 发表时间: 2022
• 期刊: IFAC-PapersOnLine
• 作者: Stefan Tauchnitz;C. Yuan;P. Stegagno

Swarm Localization Through Cooperative Landmark Identification

通过协作地标识别进行群体定位

• DOI: 10.1007/978-3-030-92790-5_33
• 发表时间: 2022
• 期刊: International Symposium Distributed Autonomous Robotic Systems
• 作者: Brent, Sarah;Yuan, Chengzhi;Stegagno, Paolo.
• 通讯作者: Stegagno, Paolo.