Fuzzy Adaptive Finite-Time Consensus Control for High-Order Nonlinear Multiagent Systems Based on Event-Triggered

This article studies the fuzzy adaptive finite-time consensus control problem for high-order nonlinear multiagent systems with unknown nonlinear dynamics. In control design,fuzzy logic systems (FLSs) are adopted to approximate the unknown nonlinear dynamics, and under the frameworks of adaptive back...

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Bibliographic Details
Published inIEEE transactions on fuzzy systems Vol. 30; no. 11; pp. 4891 - 4904
Main Authors Zhou, Haodong, Sui, Shuai, Tong, Shaocheng
Format Journal Article
LanguageEnglish
Published New York IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptive control
Artificial neural networks
Backstepping
Consensus control
Control design
Control methods
Convergence
Dynamical systems
event-triggered
Feedback control
finite-time
Fuzzy control
Fuzzy logic
Fuzzy systems
high-order nonlinear multiagent systems (MASs)
Liapunov functions
Multiagent systems
Nonlinear control
Nonlinear dynamical systems
Nonlinear dynamics
Nonlinear systems
Tracking errors
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Summary:This article studies the fuzzy adaptive finite-time consensus control problem for high-order nonlinear multiagent systems with unknown nonlinear dynamics. In control design,fuzzy logic systems (FLSs) are adopted to approximate the unknown nonlinear dynamics, and under the frameworks of adaptive backstepping recursive design and finite-time stability theory, an adaptive fuzzy finite-time consensus control method is developed. To save communication resources and reduce the numbers of controller execution times, a dynamic event-triggered mechanism with a relative threshold is established. Subsequently, an event-triggered-based finite-time fuzzy adaptive control scheme is formulated. Furthermore, by constructing novel integral-type Lyapunov functions and adding a power integrator technique, the finite-time stability of the closed-loop system and the convergence of consensus tracking errors are proved. Finally, a numerical simulation example is provided to verify the effectiveness of the proposed adaptive event-triggered consensus control method.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2022.3163907