PMID- 38109251
OWN - NLM
STAT- Publisher
LR  - 20231218
IS  - 2168-2275 (Electronic)
IS  - 2168-2267 (Linking)
VI  - PP
DP  - 2023 Dec 18
TI  - Predefined Accuracy Adaptive Tracking Control for Nonlinear Multiagent Systems 
      With Unmodeled Dynamics.
LID - 10.1109/TCYB.2023.3336992 [doi]
AB  - This article focuses on an adaptive dynamic surface tracking control issue of 
      nonlinear multiagent systems (MASs) with unmodeled dynamics and input 
      quantization under predefined accuracy. Radial basis function neural networks 
      (RBFNNs) are employed to estimate unknown nonlinear items. A dynamic signal is 
      established to handle the trouble introduced by the unmodeled dynamics. Moreover, 
      the predefined precision control is realized with the aid of two key functions. 
      Unlike the existing works on nonlinear MASs with unmodeled dynamics, to avoid the 
      issue of "explosion of complexity", the dynamic surface control (DSC) method is 
      applied with the nonlinear filter. By using the designed controller, the 
      consensus errors can gather to a precision assigned a priori. Finally, the 
      simulation results are given to demonstrate the effectiveness of the proposed 
      strategy.
FAU - Yao, Dajie
AU  - Yao D
FAU - Xie, Xiangpeng
AU  - Xie X
FAU - Dou, Chunxia
AU  - Dou C
FAU - Yue, Dong
AU  - Yue D
LA  - eng
PT  - Journal Article
DEP - 20231218
PL  - United States
TA  - IEEE Trans Cybern
JT  - IEEE transactions on cybernetics
JID - 101609393
SB  - IM
EDAT- 2023/12/18 18:41
MHDA- 2023/12/18 18:41
CRDT- 2023/12/18 12:34
PHST- 2023/12/18 18:41 [medline]
PHST- 2023/12/18 18:41 [pubmed]
PHST- 2023/12/18 12:34 [entrez]
AID - 10.1109/TCYB.2023.3336992 [doi]
PST - aheadofprint
SO  - IEEE Trans Cybern. 2023 Dec 18;PP. doi: 10.1109/TCYB.2023.3336992.