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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2023  > In Press, Accepted Manuscript
C. Zhang, Y. F. Hu, T. T. Wang, X. Gong, and H. Chen, “Data-driven iterative learning consensus tracking based on robust neural models for unknown heterogeneous nonlinear multiagent systems with input constraints,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 0, pp. 1–3, Sept. 2023.
Citation: C. Zhang, Y. F. Hu, T. T. Wang, X. Gong, and H. Chen, “Data-driven iterative learning consensus tracking based on robust neural models for unknown heterogeneous nonlinear multiagent systems with input constraints,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 0, pp. 1–3, Sept. 2023.
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Data-Driven Iterative Learning Consensus Tracking Based on Robust Neural Models for Unknown Heterogeneous Nonlinear Multiagent Systems With Input Constraints

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    • References(8)
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    W. Cao, and L. Liu, “Distributed adaptive output consensus of unknown heterogeneous non-minimum phase multi-agent systems,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 997–1008, 2023. doi: 10.1109/JAS.2023.123204
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    X. Yu, Z. Hou, and M. M. Polycarpou, “Distributed data-driven iterative learning consensus tracking for nonlinear discrete-time multiagent systems,” IEEE Trans. Autom. Control, vol. 67, no. 7, pp. 3670–3677, 2021.
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    L. Jia, L Xiao, and J. Dai, “Design and application of an adaptive fuzzy control strategy to zeroing neural network for solving time-variant QP problem,” IEEE Trans. Fuzzy Systems., vol. 29, no. 6, pp. 1544–1555, 2020.
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    H. Zhang, and F. L. Lewis, “Adaptive cooperative tracking control of higher-order nonlinear systems with unknown dynamics’,” Automatica., vol. 48, no. 7, pp. 1432–1439, 2012. doi: 10.1016/j.automatica.2012.05.008
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