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Distributed Data-Driven Iterative Learning Consensus Tracking for Nonlinear Discrete-Time Multiagent Systems
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DOI：10.1109/TAC.2021.3105653.png)
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Abstract
En 中文
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.
Keywords:
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
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
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