Distributed Time-Varying Formation Control With Obstacle Avoidance of Multiagent Systems Under Switching Topologies.

IF 10.5 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Yi Su,Yaping Sun,Xinsong Yang,Wenwu Yu,Xiaochuan Yang
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引用次数: 0

Abstract

Distributed formation tracking control with obstacle avoidance of multiagent systems (MASs) under random switching topologies and external disturbances is considered in this article. To achieve the complex objective, an effective control strategy is developed in three steps. First, under the transition probability (TP)-based mode-dependent average dwell-time (MDADT) switching topologies, a distributed objective trajectory achieves almost sure global exponential tracking of the desired formation trajectory. Second, a safe objective trajectory approach is designed by geometrically projecting the unsafe parts of the existing formation trajectory onto the boundary of the obstacle region. Finally, an integral-multiplicative barrier Lyapunov function (IMBLF) is proposed to allow agents to track the safe objective trajectory, where the IMBLF can further guarantee the safety of the MASs. One of the interesting merits of our results is that the impulsive increasing of Lyapunov function at switching instants which is necessary for classical analysis methods has been removed. The feasibility of the proposed formation control method with obstacle avoidance is verified by simulations.
切换拓扑下多智能体系统的分布式时变避障编队控制。
研究了随机交换拓扑和外部干扰下多智能体系统的分布式避障跟踪控制问题。为了实现复杂的目标,本文分三步制定了有效的控制策略。首先,在基于转移概率(TP)的模式相关平均停留时间(MDADT)切换拓扑下,分布式目标轨迹实现了几乎确定的目标地层轨迹全局指数跟踪。其次,通过将现有编队轨迹的不安全部分几何投影到障碍物区域边界上,设计了安全目标轨迹方法;最后,提出了一种积分乘障Lyapunov函数(IMBLF),使智能体能够跟踪安全的目标轨迹,从而进一步保证物体的安全。我们的结果的一个有趣的优点是去掉了李雅普诺夫函数在切换时刻的脉冲增加,这是经典分析方法所必需的。仿真结果验证了该避障编队控制方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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