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Data-Driven Bipartite Formation for a Class of Nonlinear MIMO Multiagent Systems
IEEE Transactions on Neural Networks and Learning Systems ( IF 10.4 ) Pub Date : 2021-09-29 , DOI: 10.1109/tnnls.2021.3111893
Jiaqi Liang , Xuhui Bu , Lizhi Cui , Zhongsheng Hou

The bipartite formation control for the nonlinear discrete-time multiagent systems with signed digraph is considered in this article, in which the dynamics of the agents are completely unknown and multi-input multi-output (MIMO). First, the unknown nonlinear dynamic is converted into the compact-form dynamic linearization (CFDL) data model with a pseudo-Jacobian matrix (PJM). Based on the structurally balanced signed graph, a distance-based formation term is constructed and a bipartite formation model-free adaptive control (MFAC) protocol is designed. By employing the measured input and output data of the agents, the theoretical analysis is developed to prove the bounded-input bounded-output stability and the asymptotic convergence of the formation tracking error. Finally, the effectiveness of the proposed protocol is verified by two numerical examples.

中文翻译:

一类非线性 MIMO 多智能体系统的数据驱动二分结构

本文考虑了具有符号有向图的非线性离散时间多智能体系统的二分编队控制,其中智能体的动态完全未知并且是多输入多输出(MIMO)。首先,将未知的非线性动态转换为具有伪雅可比矩阵 (PJM) 的紧凑型动态线性化 (CFDL) 数据模型。基于结构平衡符号图,构建了基于距离的编队项,并设计了二分编队无模型自适应控制(MFAC)协议。通过使用代理的测量输入和输出数据,进行理论分析以证明有界输入有界输出稳定性和编队跟踪误差的渐近收敛性。最后,
更新日期:2021-09-29
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