Abstract
In this paper, the distributed optimization problem is investigated for a class of general nonlinear model-free multi-agent systems. The dynamical model of each agent is unknown and only the input/output data are available. A model-free adaptive control method is employed, by which the original unknown nonlinear system is equivalently converted into a dynamic linearized model. An event-triggered consensus scheme is developed to guarantee that the consensus error of the outputs of all agents is convergent. Then, by means of the distributed gradient descent method, a novel event-triggered model-free adaptive distributed optimization algorithm is put forward. Sufficient conditions are established to ensure the consensus and optimality of the addressed system. Finally, simulation results are provided to validate the effectiveness of the proposed approach.
摘要
研究了一类一般非线性无模型多智能体系统的分布式优化问题. 每个智能体的动态模型是未知的, 只能获得输入和输出数据的信息. 首先, 通过采用无模型自适应控制方法, 将原来未知的非线性系统等效转化为动态线性化模型. 然后, 为保证所有智能体输出的一致性误差收敛, 提出一种基于事件触发机制的一致性控制方案. 其次, 引入分布式梯度下降法, 提出一种新的事件触发无模型自适应分布式优化算法. 根据李亚普诺夫稳定性理论, 给出闭环系统达到一致性和最优性的充分条件. 最后, 通过仿真实验验证算法设计方案的有效性.
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Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Shanshan ZHENG designed the research and drafted the paper. Licheng WANG helped organize the paper. Shanshan ZHENG and Shuai LIU revised and finalized the paper.
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Shanshan ZHENG, Shuai LIU, and Licheng WANG declare that they have no conflict of interest.
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Project supported by the National Natural Science Foundation of China (No. 62003213)
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1 Proof S1 Proof of Theorem 1
2 Proof S2 Proof of Theorem 2
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Zheng, S., Liu, S. & Wang, L. Event-triggered distributed optimization for model-free multi-agent systems. Front Inform Technol Electron Eng 25, 214–224 (2024). https://doi.org/10.1631/FITEE.2300568
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DOI: https://doi.org/10.1631/FITEE.2300568