Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 31, Issue 3, Pages 1010-1021Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2019.2915376
Keywords
Adaptive control; Synchronization; Decentralized control; Nonlinear dynamical systems; Laplace equations; Learning systems; Multi-agent systems; Cooperative control; leader-following consensus; prescribed performance; unmodeled dynamics
Categories
Funding
- National Natural Science Foundation of China [61873229, 61473250, 61773131]
- Australian Research Council [DP170102644]
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The cooperative control problem of nonlinear multiagent systems is studied in this paper. The followers in the communication network are subject to unmodeled dynamics. A fully distributed neural-networks-based adaptive control strategy is designed to guarantee that all the followers are asymptotically synchronized to the leader, and the synchronization errors are within a prescribed level, where some global information, such as minimum and maximum singular value of graph adjacency matrix, is not necessarily to be known. Based on the Lyapunov stability theory and algebraic graph theory, the stability analysis of the resulting closed-loop system is provided. Finally, an numerical example illustrates the effectiveness and potential of the proposed new design techniques.
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