Distributed adaptive consensus control for high-order multiple non-holonomic systems

2018 ◽  
Vol 93 (9) ◽  
pp. 2212-2227 ◽  
Author(s):  
Jianzhong Gu ◽  
Wuquan Li ◽  
Hongyong Yang
IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 145910-145920 ◽  
Author(s):  
Qiang Wang ◽  
Weimin Zhong ◽  
Jiapeng Xu ◽  
Wangli He ◽  
Dayu Tan

2015 ◽  
Vol 18 (2) ◽  
pp. 562-568 ◽  
Author(s):  
Xiaogang Yang ◽  
Jianxiang Xi ◽  
Jinying Wu ◽  
Zhicheng Yao

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Pinwei Li ◽  
Jiyang Dai ◽  
Jin Ying

This paper investigates adaptive fixed-time tracking consensus control problems for multiagent nonlinear pure-feedback systems with performance constraints. Compared with existing results of first/second/high-order multiple agent systems, the studied systems have more complex nonlinear dynamics with each agent being modeled as a high-order pure-feedback form. The mean value theorem is introduced to address the problem of nonaffine structure in nonlinear pure-feedback systems. Meanwhile, radial basis function neural networks (RBFNNs) are employed to approximate unknown functions. Furthermore, a constraint variable is used to guarantee that all local tracking errors are within the prescribed boundaries. It is shown that, by utilizing the proposed consensus control protocol, each tracking consensus error can converge into a neighborhood around zero within designed fixed time, the tracking consensus performance can be ensured during the whole process, and all signals in the investigated systems are bounded. Finally, two simulations are performed and the results demonstrate the effectiveness of the proposed control strategy.


Author(s):  
Wenjing Xie ◽  
Baoli Ma ◽  
Tyrone Fernando ◽  
Wei Huang ◽  
Yixin Zhao

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