多智能体系统的一致性数据驱动最优迭代学习控制

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中图分类号：TP13 文献标志码：A DOI：10.13338/j.issn.1674-649x.2025.02.014

Data-driven optimal iterative learning control for the consensus of multi-agent systems

GENG Yan，CHANG Duhui，HE Xingshi （School ofScience，Xi'anPolytechnic University，Xi'an 7loo48，China）

Abstract To improve the tracking performance of multi-agent systems and relax the convergence condition of the algorithm，a data-driven optimal iterative learning control strategy is proposed. For a class of linear time-invariant multi-agent systems，a parameter estimation algorithm is constructed to estimate the system parameters by minimizing the residual error between the predicted output and the actual output，as wellas the difference between adjacent estimates. The virtual leader is used instead of expected trajectory，based on the communication topology，the optimal iterative learning control law is designed by optimizing the index function of the agent's consistent tracking error and control input diference，meanwhile，incorporating estimated parameters into the learning process. The results show that the error of parameter estimation is bounded and the tracking error of the system converges monotonically. The numerical simulations validate the effectiveness of this designed control strategy.

Keywordsiterative learning control; multi-agent systems; data-driven; parameter estimation algorithm;optimal control

0引言

迭代学习控制（iterativelearningcontrol，ILC）是一种模仿人类通过学习经验获取知识的智能技术，适用于具有重复运动特性的被控系统。（剩余12077字）