西安工程大学学报2025，Vol.39Issue(2)：118-126,9.DOI:10.13338/j.issn.1674-649x.2025.02.014
多智能体系统的一致性数据驱动最优迭代学习控制
Data-driven optimal iterative learning control for the consensus of multi-agent systems
摘要
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 con-structed to estimate the system parameters by minimizing the residual error between the predic-ted output and the actual output,as well as the difference between adjacent estimates.The virtual leader is used instead of expected trajectory,based on the communication topology,the optimal it-erative learning control law is designed by optimizing the index function of the agent's consistent tracking error and control input difference,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 ef-fectiveness of this designed control strategy.关键词
迭代学习控制/多智能体系统/数据驱动/参数估计算法/最优控制Key words
iterative learning control/multi-agent systems/data-driven/parameter estimation al-gorithm/optimal control分类
信息技术与安全科学引用本文复制引用
耿燕,常杜辉,贺兴时..多智能体系统的一致性数据驱动最优迭代学习控制[J].西安工程大学学报,2025,39(2):118-126,9.基金项目
陕西省自然科学基础研究计划项目(2020JQ-831) （2020JQ-831）
陕西省教育厅专项科研计划项目(20JK0642) （20JK0642）
