Consensus tracking for nonlinear multi-agent systems with unknown disturbance by using model free adaptive iterative learning control
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DOI: 10.1016/j.amc.2019.124701
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Cited by:
- Zhao, Huarong & Peng, Li & Yu, Hongnian, 2022. "Quantized model-free adaptive iterative learning bipartite consensus tracking for unknown nonlinear multi-agent systems," Applied Mathematics and Computation, Elsevier, vol. 412(C).
- Qi, Yiwen & Qu, Ziyu & Yao, Zhaohui & Zhao, Xiujuan & Tang, Yiwen, 2023. "Event-Triggered iterative learning control for asynchronously switched systems," Applied Mathematics and Computation, Elsevier, vol. 440(C).
- Zhu, Lin & Che, Wei-Wei & Jin, Xiao-Zheng, 2022. "Dynamic event-triggered tracking control for model-free networked control systems," Applied Mathematics and Computation, Elsevier, vol. 431(C).
- Zhang, Weijian & Du, Haibo & Chu, Zhaobi, 2022. "Robust discrete-time non-smooth consensus protocol for multi-agent systems via super-twisting algorithm," Applied Mathematics and Computation, Elsevier, vol. 413(C).
- Hou, Rui & Cui, Lizhi & Bu, Xuhui & Yang, Junqi, 2021. "Distributed formation control for multiple non-holonomic wheeled mobile robots with velocity constraint by using improved data-driven iterative learning," Applied Mathematics and Computation, Elsevier, vol. 395(C).
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Keywords
Multi-agent systems (MAS); Consensus tracking; Model-free adaptive iterative learning control (MFAILC); Disturbance compensation;All these keywords.
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