报告时间：2023年12月19日（周二）上午10:00-12:00
报告地点：船电楼A306会议室
报告摘要： Physical attackers pose significant danger to the operation of intelligent vehicles, especially multiagent systems (MASs). Therefore, how to guarantee safety and performance of MAS in the presence of physical attackers is an important topic to be addressed. State-of-the-art literature on constrained multiagent system operations can only deal with constant or at best time-varying constraint requirements. Such constraint formulations cannot respond well to the dynamic environment and presence of physical attackers. In this work, we consider a formation tracking problem for a group of unmanned aerial vehicles (UAVs) in the presence of a physical attacker. The safety/performance constraint functions are environment-aware and dynamic in nature, whose formulation depends on certain path parameters and presence of the attacker. The dependence on path ensures adaptation to the dynamic operation environment. The dependence on the attacker ensures swift adjustment based on the relative distances between the attacker and agents. UAV desired paths and desired path speeds can also be both path- and attacker-dependent. A framework where composite barrier functions are incorporated with path parameter timing laws has been proposed to address the safety and performance considerations. Adaptive laws and neural networks are used to approximate unknown attacker velocity, unknown system parameters and external disturbances are estimated by adaptive laws. The proposed formation architecture can ensure formation tracking errors converge exponentially to small neighborhoods near the equilibrium, with all constraint requirements met. At the end a simulation study further illustrates the proposed scheme and demonstrates its efficacy.
报告专家简介: 金旭博士，美国肯塔基大学助理教授、博士生导师。2013年获得新加坡国立大学电子计算机专业一等荣誉学士学位，2015年获得加拿大多伦多大学电子计算机专业硕士学位，2018年获得美国佐治亚理工大学数学硕士学位，2019年获得美国佐治亚理工大学航天工程博士学位。2019年至今在美国肯塔基大学机械工程系工作。发表论文60余篇，引用量3300余次。独立主持美国国家自然科学基金（NSF）项目一项，并多次担任美国国家自然科学基金委评审专家。另主持美国航空航天局（NASA）州级项目一项。金博士在各个领域学科综合排名的斯坦福全球“2022年度科学影响力排行榜”排名7562。
船舶电气工程学院
2023年12月18日
