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Adaptive learning control synchronization for unknown time-varying complex dynamical networks with prescribed performance

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Abstract

This paper proposes a prescribed performance adaptive learning control scheme for complex dynamical networks. It can ensure that the states of all nodes in the complex dynamical networks can synchronize to the specified target trajectory, and satisfy prescribed performance constraints. Based on Lyapunov stability theory, it is proved that all signals in the closed-loop systems are bounded and the synchronization errors converge to a prescribed residual set. Simulation results are presented to show the validity of the proposed approach.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61603286 and 61573013; the Fundamental Research Funds for the Central Universities under Grant No. JB160702.

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Authors and Affiliations

  1. Department of Applied Mathematics, Xidian University, Xi’an, 710071, People’s Republic of China

    Aili Fan & Junmin Li

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  1. Aili Fan
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  2. Junmin Li
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Correspondence to Junmin Li.

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Fan, A., Li, J. Adaptive learning control synchronization for unknown time-varying complex dynamical networks with prescribed performance. Soft Comput 25, 5093–5103 (2021). https://doi.org/10.1007/s00500-020-05511-5

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