期刊
NEUROCOMPUTING
卷 349, 期 -, 页码 64-76出版社
ELSEVIER
DOI: 10.1016/j.neucom.2019.04.018
关键词
Distributed consensus control; Neural networks; Input quantization; Actuator fault; Consensus asymptotic convergence
资金
- National Natural Science Foundation [61374040, 61673277]
This paper investigates the consensus asymptotic convergence problem for a class of nth-order strict-feedback multiagent systems, which include the input quantization, actuator faults, unknown nonlinear functions and directed communication topology. Because the upper bounds of the time-varying stuck faults and external disturbance are commonly difficult to accurately determine, it is also assumed that these upper bounds are unknown in this paper. First, a group of first-order filters are designed to estimate the bounds of the reference signal for each agent. Second, smooth functions are introduced to compensate the effect of quantization and bounded stuck faults. Meanwhile, a new back-stepping method is used to propose an intermediate control law and an adaptive design procedure, and the final distributed control protocols are established. All closed-loop signals are uniformly bounded, and the tracking errors asymptotically converge to zero. Finally, a practical example simulation is provided to demonstrate the effectiveness of the proposed scheme. (C) 2019 Elsevier B.V. All rights reserved.
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