期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 67, 期 7, 页码 3670-3677出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2021.3105653
关键词
Control systems; Data models; Trajectory; Heuristic algorithms; Topology; Task analysis; Nonlinear dynamical systems; Consensus tracking; data-driven iterative learning control; dynamic linearization technique; nonlinear repetitive discrete-time multiagent systems
资金
- National Natural Science Foundation of China [61433002, 61833001]
- European Unions Horizon 2020 research and innovation programme [739551]
- Republic of Cyprus
In this article, a data-driven distributed leader-follower iterative learning consensus tracking control approach is proposed for unknown repetitive nonlinear nonaffine discrete-time multi-agent systems. The approach achieves distributed consensus control among agents with limited information.
In this article, a data-driven distributed leader-follower iterative learning consensus tracking control approach is proposed for unknown repetitive nonlinear nonaffine discrete-time multi-agent systems. The leader's command is only communicated to a subset of the following agents and each following agent exchanges information only with its neighbors under a directed graph. A local iterative learning consensus control protocol is designed using only local measurements communicated among neighboring agents without the availability of physical and structural information of each agent by virtue of the dynamic linearization method both on the agent and the ideal distributed learning controller along the iteration axis. The convergent consensus properties of the tracking errors along the iteration axis are rigorously established under the strongly connected iteration-independent and iteration-varying communication topologies. One example is provided to validate the effectiveness of the proposed iterative learning consensus control protocol.
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