Hybrid Predictive Control for Distributed Multi-agent Systems

分布式多智能体系统的混合预测控制

• 批准号: 1710621
• 负责人: Ricardo Sanfelice
• 金额: $ 36.04万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1710621&HistoricalAwards=false
• 关键词: Hybrid; Predictive; Control; Distributed; Multi

This proposal presents a research plan to advance the knowledge on the systematic design of algorithms that use prediction and optimization to make distributed decisions in multi-agent systems. Due to the combination of different types of dynamics (continuous and discrete) emerging from the physics laws governing the behavior of the systems, the networks that link them, and their on-board computing systems, the multi-agent systems are modeled as hybrid dynamical systems. The combination of such mixed behavior, both in the system to control and in the algorithms, is embodied in key future networks of multi-agent systems. The future smart grid will have variables that change continuously according to electric circuit laws, exhibit jumps due to controlled switches, failures, and modeling approximations, while the control algorithms require logic to adapt to such abrupt changes. Hybrid behavior will also emerge in other networked multi-agent systems, such as self-driving cars and groups of autonomous aerial vehicles, in particular, due to communication events, abrupt changes in connectivity, and the cyber-physical interaction between agents/robots, their environment, and communication networks. The results from this project will enable the development of such networked multi-agent systems with simultaneous robustness and optimality.The impact of the proposed research plan stems from a novel use of hybrid prediction in the controllers, one that guarantees simultaneous robust and optimal behavior of the closed-loop system. The proposed hybrid prediction approach efficiently exploits key robust stabilization capabilities of hybrid feedback control and optimality guarantees of receding horizon control. The design of the control algorithms will employ Lyapunov-based and optimization techniques suitable to deal with the hybrid dynamics emerging from the system to control or the algorithm. The proposed hybrid prediction technique will lead to novel tools for systematic design of control and communication algorithms for distributed hybrid systems prediction is a feature currently lacking in hybrid control theory. These new tools will pave the road for the design of distributed algorithms that operate robustly and optimally when applied to real-world systems. The proposed research plan is deeply integrated with teaching and training activities that will significantly impact middle and high school education levels by training students on control engineering, hybrid systems, cyber-physical systems, and applications to networked multi-agent systems. A plan to improve existing courses will incorporate state-of-the-art material on modeling and predictive control in the classroom. Participation in these activities of underrepresented groups will provide significant broad impact to the overall project. Broad dissemination will occur through educational activities, workshops, local industry, and international partnerships.

该建议提出了一个研究计划，以提高知识的系统设计的算法，使用预测和优化，使分布式决策多智能体系统。由于不同类型的动力学（连续和离散）的组合出现的物理定律的行为的系统，连接它们的网络，和他们的车载计算系统，多智能体系统建模为混合动力系统。这种混合行为的组合，无论是在系统控制和算法，体现在未来的多智能体系统的关键网络。未来的智能电网将具有根据电路定律连续变化的变量，由于受控开关、故障和建模近似而呈现跳跃，而控制算法需要逻辑来适应这种突然变化。混合行为也将出现在其他联网的多智能体系统中，例如自动驾驶汽车和自动飞行器组，特别是由于通信事件，连接的突然变化以及智能体/机器人之间的网络物理交互，它们的环境和通信网络。从这个项目的结果将使这种网络化的多智能体系统的发展，同时鲁棒性和optimality.The的影响，拟议的研究计划源于一种新的使用混合预测的控制器，一个保证同时鲁棒性和最优行为的闭环系统。所提出的混合预测方法有效地利用关键的鲁棒稳定能力的混合反馈控制和滚动时域控制的最优性保证。控制算法的设计将采用基于Lyapunov的优化技术，适合于处理从系统中出现的混合动力学，以控制或算法。建议的混合预测技术将导致新的工具，系统设计的控制和通信算法的分布式混合系统预测是目前缺乏混合控制理论的功能。这些新工具将为分布式算法的设计铺平道路，这些算法在应用于现实世界的系统时具有鲁棒性和最佳性。拟议的研究计划与教学和培训活动紧密结合，通过培训学生控制工程，混合动力系统，网络物理系统以及网络多智能体系统的应用，将对初中和高中教育水平产生重大影响。一项改善现有课程的计划将在课堂上使用最先进的建模和预测控制材料。代表性不足的群体参与这些活动将对整个项目产生重大而广泛的影响。将通过教育活动、讲习班、地方工业和国际伙伴关系进行广泛传播。

项目成果

Robust distributed synchronization of networked linear systems with intermittent information

• DOI: 10.1016/j.automatica.2019.03.020
• 发表时间: 2019-07
• 期刊: Autom.
• 作者: S. Phillips;R. Sanfelice

Sufficient Conditions for Temporal Logic Specifications in Hybrid Dynamical Systems

混合动力系统中时态逻辑规范的充分条件

• DOI: 10.1016/j.ifacol.2018.08.017
• 发表时间: 2018
• 期刊: IFAC-PapersOnLine
• 作者: Han, Hyejin;Sanfelice, Ricardo G.
• 通讯作者: Sanfelice, Ricardo G.

Hybrid attack monitor design to detect recurrent attacks in a class of cyber-physical systems

混合攻击监视器设计，用于检测一类网络物理系统中的重复攻击

• DOI: 10.1109/cdc.2017.8263845
• 发表时间: 2017
• 期刊: Proceedings of 2017 IEEE 56th Annual Conference on Decision and Control (CDC 2017
• 作者: Phillips, Sean;Duz, Alessandra;Pasqualetti, Fabio;Sanfelice, Ricardo G.
• 通讯作者: Sanfelice, Ricardo G.

A unifying convex analysis and switching system approach to consensus with undirected communication graphs

• DOI: 10.1016/j.automatica.2019.108598
• 发表时间: 2020-01-01
• 期刊: AUTOMATICA
• 影响因子: 6.4
• 作者: Goebel, Rafal;Sanfelice, Ricardo G.
• 通讯作者: Sanfelice, Ricardo G.

Analyzing Action Games: A Hybrid Systems Approach

分析动作游戏：混合系统方法

• DOI: 10.1145/3337722.3337757
• 发表时间: 2019
• 期刊: Proceedings of the Foundation of Digital Games Conference
• 作者: Zeleke, Y;Osborn, J;Sanfelice, RG
• 通讯作者: Sanfelice, RG