非线性分数阶多智能体系统的自适应输出一致性：一种分数阶backstepping方法

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, THEORY & METHODS

International Journal of General Systems Pub Date : 2022-11-06 DOI:10.1080/03081079.2022.2132488

Milad Shahvali, Ali Azarbahram, N. Pariz

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引用次数: 0

摘要

针对一类存在未知动力学的分数阶严格反馈非线性多智能体系统，采用backstepping策略进行了分布式控制设计。考虑到反馈设计中跟随者状态信息不完全可测量，引入分数阶无限维神经网络状态观测器来估计不可用状态。为了消除未知非线性函数的不良影响，还提出了无限维神经自适应律。此外，基于李雅普诺夫分数阶稳定性方法和图论，与现有结果不同，设计了一种基于分布式神经自适应观测器的控制结构，以确保所有闭环网络信号最终都是有界的。最后，通过仿真实例验证了该控制方法的有效性。

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Adaptive output consensus of nonlinear fractional-order multi-agent systems: a fractional-order backstepping approach

This paper presents the distributed control design for a class of fractional-order strict-feedback nonlinear multi-agent systems in the presence of unknown dynamics by employing backstepping strategy. Considering that the information of followers’ states are not fully measurable for feedback design, the fractional-order infinite-dimension neural-network state observer is introduced to estimate the unavailable states. The infinite-dimension neuroadaptive laws are also proposed to eliminate the undesirable effects of the unknown nonlinear functions. Besides, based on the Lyapunov fractional-order stability approach and graph theory, unlike the existing results, a distributed neural adaptive observer-based control architecture is designed to ensure that all the closed-loop network signals are ultimately bounded. Finally, a simulation example is given to demonstrate the validity of the proposed control method.

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来源期刊

International Journal of General Systems 工程技术-计算机：理论方法

CiteScore

4.10

自引率

20.00%

发文量

审稿时长

6 months

期刊介绍： International Journal of General Systems is a periodical devoted primarily to the publication of original research contributions to system science, basic as well as applied. However, relevant survey articles, invited book reviews, bibliographies, and letters to the editor are also published. The principal aim of the journal is to promote original systems ideas (concepts, principles, methods, theoretical or experimental results, etc.) that are broadly applicable to various kinds of systems. The term “general system” in the name of the journal is intended to indicate this aim–the orientation to systems ideas that have a general applicability. Typical subject areas covered by the journal include: uncertainty and randomness; fuzziness and imprecision; information; complexity; inductive and deductive reasoning about systems; learning; systems analysis and design; and theoretical as well as experimental knowledge regarding various categories of systems. Submitted research must be well presented and must clearly state the contribution and novelty. Manuscripts dealing with particular kinds of systems which lack general applicability across a broad range of systems should be sent to journals specializing in the respective topics.