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.

该建议提出了一项研究计划，以提高有关使用预测和优化来在多代理系统中做出分布式决策的算法设计的知识。由于从管理系统行为，链接它们的网络以及它们的板载计算系统的物理定律中出现了不同类型的动态（连续和离散）的组合，因此将多代理系统建模为混合动力学系统。在控制和算法中，这种混合行为的结合体现在多代理系统的关键未来网络中。未来的智能电网将具有根据电路定律不断变化的变量，由于受控开关，故障和建模近似值而显示出跳跃，而控制算法则需要逻辑以适应这种突然的变化。混合行为还将出现在其他网络多机构系统中，例如自动驾驶汽车和自动驾驶汽车组，特别是由于通信事件，连通性的突然变化以及代理/机器人/机器人之间的网络物理交互，环境和通信网络之间的网络物理交互。该项目的结果将能够开发具有同时鲁棒性和最佳性的网络多代理系统。拟议的研究计划的影响源于控制器中混合预测的新颖使用，该预测保证了闭环系统的同时强大和最佳行为。拟议的混合预测方法有效利用了混合反馈控制的关键稳定能力，并保证了恢复地平线控制的最佳保证。控制算法的设计将采用基于lyapunov的和优化技术，以处理从系统到控制或算法的混合动力学。拟议的混合预测技术将为分布式混合系统预测的控制和通信算法的系统设计提供新的工具，这是当前缺乏混合控制理论的功能。这些新工具将为设计分布式算法的设计铺平道路，这些算法在应用于现实世界系统时可靠，最佳地运行。拟议的研究计划与教学和培训活动深入融合，这些活动将通过培训学生对控制工程，混合系统，网络物理系统以及对网络多代理系统的应用来重大影响中学和高中的教育水平。改善现有课程的计划将结合教室中的建模和预测控制的最新材料。参与代表性不足的群体的这些活动将对整个项目产生重大影响。广泛的传播将通过教育活动，研讨会，地方工业和国际伙伴关系发生。

项目成果

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.

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.

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.

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

Hybrid Control for Robust and Global Tracking on a Smooth Manifold

在平滑流形上实现稳健和全局跟踪的混合控制

• DOI: 10.1109/tac.2019.2927708
• 发表时间: 2019
• 期刊: IEEE transactions on automatic control
• 影响因子: 6.8
• 作者: Casau, P;Cunha, R;Sanfelice, RG;Silvestre, C
• 通讯作者: Silvestre, C