Adaptive Neural Control for Novel Constrained Nonlinear Nonstrict Feedback Mixed MASs via Command Filter

被引:0
作者
Hua, Yu [1 ,2 ]
Zhang, Tianping [1 ,2 ]
Duan, Yanan [1 ]
Zhu, Jiasong [1 ]
机构
[1] Yangzhou Univ, Coll Informat Engn, Yangzhou 225127, Jiangsu, Peoples R China
[2] Yangzhou Univ, Coll Math Sci, Yangzhou 225002, Jiangsu, Peoples R China
来源
PROCEEDINGS OF 2024 CHINESE INTELLIGENT SYSTEMS CONFERENCE, VOL II, CISC 2024 | 2024年 / 1284卷
基金
中国国家自然科学基金;
关键词
Dynamic surface control; Mixed multiagent systems; Nonlinear transformation rules; DYNAMIC SURFACE CONTROL;
D O I
10.1007/978-981-97-8654-1_58
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, dynamic surface control (DSC) with radial basis function neural networks (RBFNNs) is addressed for the novel nonlinear mixed multiagent systems with constraints. Each agent can be a nonstrict state or output feedback system. By using the nonlinear transformation rules (NTRs), the states or output constraints can be handled. By using the compensating signals, the filtering errors can be eliminated. The stability analysis demonstrates that all the signals are bounded.
引用
收藏
页码:584 / 592
页数:9
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