植绒(纹理)
自适应控制
控制理论(社会学)
操作员(生物学)
计算机科学
多智能体系统
碰撞
控制(管理)
控制工程
投影(关系代数)
人工智能
工程类
算法
物理
生物
计算机安全
基因
转录因子
抑制因子
量子力学
生物化学
作者
Ximing Wang,Zhitao Li,Zixing Wu
摘要
Abstract Adaptive flocking control of multi‐agent systems faces challenges in handling uncertainties and ensuring safety. This paper aims to address these issues based on the hypothesis that the uncertain parameters are bounded. First, a concurrent learning adaptive control method relaxes the persistently excitation condition for parameter convergence, enabling adaptability with interval excitation only. Second, an element‐wise projection operator bounds parameter estimates within known intervals, precomputing collision avoidance conditions, and guaranteeing safety. Third, combining with the aforementioned methods, a distributed flocking algorithm incorporates limited sensing range in a moving region, achieving collision avoidance, connectivity, and cohesion via bounded potential functions. LaSalle's invariance principle shows that parameter estimates converge within bounds, collision avoidance conditions hold, and system stability is achieved. Simulations validate enhanced adaptability, guaranteed safety, and the expected cooperative flocking motion. The proposed approach addresses critical challenges for real‐world deployment of swarm technology.
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