Fixed-Time Distributed Average Tracking for a Class of Nonlinear Multiagent Systems With Unity Relative Degree

IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Qingpeng Liang;Deqing Huang;Lei Ma;Jiangping Hu;Linying Xiang;Yanzhi Wu
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引用次数: 0

Abstract

This article investigates the fixed-time distributed average tracking (DAT) problem for nonlinear multiagent systems with unity relative degree under external disturbances. A distributed control framework is developed to guarantee fixed-time convergence of all agents’ outputs to the target trajectory, which is defined as the average of multiple nonlinear reference signals. The approach consists of three main components. First, a steady-state generator is introduced to reconstruct the desired trajectory. Using this generator, a distributed observer is designed to estimate the target trajectory while ensuring robustness against initialization errors. Subsequently, an observer-based output-feedback controller is developed to guarantee the convergence of each agent’s output to its corresponding reference signal within a fixed time. Through rigorous theoretical analysis, it is proved that the proposed control architecture ensures fixed-time convergence to the target trajectory, effectively solving the fixed-time DAT problem. The effectiveness of the proposed method is validated through numerical simulations.
一类具有统一关联度的非线性多智能体系统的固定时间分布平均跟踪
研究了具有统一相对度的非线性多智能体系统在外界干扰下的固定时间分布平均跟踪问题。为了保证所有智能体输出到目标轨迹的固定时间收敛,提出了一种分布式控制框架,将其定义为多个非线性参考信号的平均值。该方法由三个主要部分组成。首先,引入稳态发生器来重建期望轨迹。利用该生成器,设计了一个分布式观测器来估计目标轨迹,同时确保对初始化误差的鲁棒性。随后,设计了一种基于观测器的输出反馈控制器,以保证每个智能体的输出在固定时间内收敛到对应的参考信号。通过严密的理论分析,证明了所提出的控制体系结构保证了对目标轨迹的定时收敛,有效地解决了定时DAT问题。通过数值仿真验证了该方法的有效性。
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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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