Finite-time Containment Control for Lagrangian Networks

拉格朗日网络的有限时间遏制控制

• 批准号: 1213295
• 负责人: Wei Ren
• 金额: $ 27.33万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-11-04 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1213295&HistoricalAwards=false
• 关键词: Finite; time; Containment; Control; Lagrangian

Objective:The objective of this research is to address the problem of distributed finite-time containment control for Lagrangian networks. Despite the fact that each topic of containment control, finite-time control of networked systems, and control of Lagrangian networks is crucial for networked multi-agent systems, there are few existing results on each topic not to mention a combination of the three due to numerous inherent challenges. This research addresses a combination of the three topics in a unified framework.Intellectual Merit:Distributed algorithms will be designed to guarantee finite-time containment control for Lagrangian networks with stationary leaders, dynamic leaders, and swarming behavior and will be experimentally validated on heterogeneous robotic networks. The novelty of the research lies in the paradigm changes i) from leaderless or single leader behaviors dominantly studied in multi-agent systems to more realistic but complicated multi-leader multi-follower behaviors involving multi-layer collaboration between multiple heterogeneous teams of agents, ii) from distributed algorithms that can only guarantee asymptotic or at most exponential convergence in infinite time dominantly explored in multi-agent systems to distributed algorithms that are guaranteed to converge in finite time to meet the needs of many time-critical missions, and iii) from simplified/idealized linear point models dominantly adopted in multi-agent systems to more realistic and prevalent nonlinear Lagrangian models that represent a large class of mechanical systems including autonomous vehicles and robotic manipulators.Broader Impacts: The research has impact on numerous civilian, homeland security, and military applications and on other fields related to networked systems including biology, sociology, and physics. A capstone course on autonomous systems will be developed with emphasis on hands-on labs and campus-wide robotics competitions. Outreach activities to high school students will be pursued through involvement in a state-wide engineering program in Utah, Engineering State. Broad dissemination of the research results will be accomplished through publications, tutorial workshops, a summer school, and a dedicated website.

目的：本研究的目的是解决拉格朗日网络的分布式有限时间遏制控制问题。尽管遏制控制、网络系统的有限时间控制和拉格朗日网络控制的每个主题对于网络多智能体系统都至关重要，但每个主题的现有结果很少，更不用说这三个主题的组合了，因为许多固有的挑战。这项研究将三个主题结合在一个统一的框架中。 智力优点：分布式算法将被设计为保证具有固定领导者、动态领导者和集群行为的拉格朗日网络的有限时间遏制控制，并将在异构机器人上进行实验验证网络。该研究的新颖性在于范式的变化i）从多智能体系统中主要研究的无领导者或单一领导者行为到更现实但复杂的多领导者多追随者行为，涉及多个异构代理团队之间的多层协作，ii ）从多智能体系统中主要探索的只能保证在无限时间内渐近或至多指数收敛的分布式算法到保证在有限时间内收敛以满足许多时间关键任务的需要的分布式算法，以及iii）多智能体系统中主要采用的简化/理想化线性点模型到更现实和普遍的非线性拉格朗日模型，这些模型代表了一大类机械系统，包括自动驾驶车辆和机器人操纵器。更广泛的影响：该研究对许多民用、国土安全、和军事应用以及与网络系统相关的其他领域，包括生物学、社会学和物理学。将开发有关自主系统的顶点课程，重点是实践实验室和校园范围内的机器人竞赛。将通过参与犹他州工程州的全州工程项目来开展针对高中生的外展活动。研究成果的广泛传播将通过出版物、辅导研讨会、暑期学校和专门网站来完成。