结构不平衡拓扑下非仿射非线性质量的自适应规定有限时间二部一致控制

IF 8.6 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Xiaomei Wang;Yongduan Song;Xudong Zhao;Huanqing Wang;Ding Wang;Ben Niu
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引用次数: 0

摘要

研究了在不平衡通信拓扑下,一类具有有限时间跟踪性能的异构非仿射非线性多智能体系统的自适应二部一致性跟踪控制算法。在不平衡有向图的情况下,提出了一种新的局部最优双划分策略,将不平衡的通信拓扑转化为结构平衡的通信拓扑,从而实现了二部共识跟踪控制。为了达到预期的跟踪性能,设计思想侧重于开发一个规定的有限时间性能函数(PFTPF),能够事先精确地预先分配收敛时间和精度。所探索的自适应控制算法可以保证在给定的有限时间内,有关闭环质量的整个信号保持有界,而二部一致误差收敛到零附近的预定范围内。最后,对机器人系统进行了仿真,验证了设计方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Prescribed Finite-Time Bipartite Consensus Control for Nonaffine Nonlinear MASs Under Structurally Unbalanced Topology
This article investigates an adaptive bipartite consensus tracking control algorithm for a class of heterogeneous nonaffine nonlinear multiagent systems (MASs) with prescribed finite-time tracking performance under an unbalanced communication topology. In the case of an unbalanced digraph, a novel locally optimal bipartition strategy is proposed to transform the unbalanced communication topology into a structurally balanced one, thereby enabling the implementation of bipartite consensus tracking control. To achieve the expected tracking performance, the design philosophy focuses on developing a prescribed finite-time performance function (PFTPF), capable of preassigning the convergence time and accuracy precisely beforehand. The explored adaptive control algorithm can ensure that the whole signals concerning the closed-loop MASs remain bounded while the bipartite consensus errors converge to a predetermined range around zero within the prescribed finite time. Ultimately, the simulation results on robotic systems prove the availability of the developed design solution.
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来源期刊
IEEE Transactions on Systems Man Cybernetics-Systems
IEEE Transactions on Systems Man Cybernetics-Systems AUTOMATION & CONTROL SYSTEMS-COMPUTER SCIENCE, CYBERNETICS
CiteScore
18.50
自引率
11.50%
发文量
812
审稿时长
6 months
期刊介绍: The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.
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