具有未知时变控制方向和输入延迟的多智能体系统的事件触发一致性跟踪。

IF 10.5 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Zhiwei Hua,Shengyuan Xu,Deming Yuan
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引用次数: 0

摘要

本文研究了具有多个未知数的复杂异构多智能体系统的事件触发共识跟踪控制问题。所考虑的系统涉及完全未知的时变控制方向(cd),未知的时变输入延迟(utvd)和功能,使问题变得特别具有挑战性。为了减少utvd的影响,我们构建了一个辅助系统来产生补偿信号。在此基础上,通过回溯法和一系列Nussbaum函数,提出了一种新的自适应比例积分(PI)控制方法。结果表明,该跟踪误差能够满足预定的暂态和稳态性能准则,保证了闭环系统中所有信号的渐近跟踪和全局有界性。该方案的主要优点在于控制器设计简单,控制性能提高,不需要未知函数的先验信息。最后,通过仿真实例验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Event-Triggered Consensus Tracking for Multiagent Systems With Unknown Time-Varying Control Directions and Input Delays.
This article addresses the event-triggered consensus tracking control problem for complex heterogeneous multiagent systems with multiple unknown. The systems under consideration involve fully unknown time-varying control directions (CDs), unknown time-varying input delays (UTVDs) and functions, making the problem particularly challenging. To reduce the effects of UTVDs, an auxiliary system is constructed to produce a compensation signal. Building upon this, a novel adaptive proportional-integral (PI) control approach is developed through the backstepping method and a series of Nussbaum functions. It is demonstrated that the tracking error can meet predefined transient and steady-state performance criteria, ensuring asymptotic tracking and global boundedness of all signals in the closed-loop system. The key advantage of this solution lies in its simplicity of controller design and improved control performance, without requiring prior information about the unknown functions. Finally, a simulation example validates the validity of the proposed approach.
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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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