Adaptive 2-bits-triggered neural control for uncertain nonlinear multi-agent systems with full state constraints

• Published in: Neural networks Vol. 153; pp. 37 - 48
• Main Authors: Chen, Zicong, Wang, Jianhui, Zou, Tao, Ma, Kemao, Wang, Qinruo
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.09.2022
• Subjects: 2-bits-triggered control; Adaptive control; Consensus control; Full state constraints; Multi-agent systems (MASs); Consensus control; Adaptive control; Full state constraints; Multi-agent systems (MASs); 2-bits-triggered control
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2022.05.019

This paper investigates an adaptive 2-bits-triggered neural control for a class of uncertain nonlinear multi-agent systems (MASs) with full state constraints. Considering the limitations of practical physical devices and operating conditions, MASs may suffer performance degradation or even crash while the system states are not restricted. With this in mind, combined with barrier Lyapunov function (BLF), an adaptive neural consensus control is developed to guarantee that the state constraints of all followers are not violated. Further, the conversion relationship between the state constraints of MASs and the synchronization error constraints is clarified more precisely, which could improve the synchronization performance of MASs. In addition, considering both trigger threshold setting and control signal transmission bits issues, a 2-bit trigger strategy is proposed to maximize the utilization of MASs bandwidth resources. Theoretical analysis shows that all signals are uniformly ultimately bounded. And the simulation results demonstrate its effectiveness.