| 引用本文: | 李枝强,刘洋,周琪,鲁仁全.精确估计下的多智能体系统漏斗复合控制[J].控制理论与应用,2022,39(8):1417~1425.[点击复制] |
| Li Zhi-qiang,LIU Yang,ZHOU Qi,LU Ren-quan.Funnel composite control for multi-agent systems with accurate estimation[J].Control Theory and Technology,2022,39(8):1417~1425.[点击复制] |
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| 精确估计下的多智能体系统漏斗复合控制 |
| Funnel composite control for multi-agent systems with accurate estimation |
| 摘要点击 4172 全文点击 593 投稿时间：2021-08-18 修订日期：2021-11-29 |
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| DOI编号 10.7641/CTA.2021.10756 |
| 2022,39(8):1417-1425 |
| 中文关键词 多智能体系统 神经网络 扰动观测器 复合估计模型 |
| 英文关键词 multi-agent systems neural network disturbance observer composite estimation model |
| 基金项目 国家自然科学基金项目(62121004, 62003097, 61973091),“广东特支计划”本土创新创业团队项目(2019BT02X353), 广东省重点领域研发计划 项目(2021B0101410005), 广东省研究生教育创新计划项目(2019JGXM40)资助. |
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| 中文摘要 |
| 针对一类具有时变扰动的非线性多智能体系统, 研究其在有向拓扑下的一致性跟踪问题, 提出一种基于精
确估计的复合自适应预设有限时间(PFT)漏斗控制方法. 首先, 构建一种新的PFT漏斗控制, 使跟踪误差约束在PFT
漏斗边界内. 其次, 采用神经网络(NN)逼近系统的未知非线性, 并利用NN逼近信息设计扰动观测器, 建立基于NN和
扰动观测器的复合估计模型, 将得到的预测误差引入NN权值的复合更新律中, 实现对未知非线性和时变扰动的精
确估计. 然后, 利用动态面技术和误差补偿机制, 在解决传统反步法“计算爆炸”问题的同时, 消除滤波器误差对系
统的影响. 最后, 通过Lyapunov稳定性理论证明闭环系统所有信号均为有界的, 并通过仿真实验验证控制方法的有
效性. |
| 英文摘要 |
| For a class of nonlinear multi-agent systems with time-varying disturbances, the problem of consensus tracking
in directed topology is studied, and a composite adaptive preassigned finite-time (PFT) funnel control based on accurate
estimation is proposed. Firstly, a new PFT funnel control is used to constrain the tracking error within the boundary of PFT
funnel. Secondly, neural networks (NNs) are employed to approximate the unknown nonlinearities of the system, and the
disturbance observer is constructed with NN approximation information. The composite estimation model with NN and
the disturbance observer is built to obtain the prediction error. The prediction error is introduced into the NN weight updating
composite law to accurately estimate the unknown nonlinearities and time-varying disturbances. Then, the dynamic
surface control technique is utilized to solve the “explosion of complexity” problem caused by the traditional backstepping
technique, and the error compensation method is utilized to solve the influence of filter errors. Finally, according to the
Lyapunov stability theory, all signals in the closed-loop system are bounded, and the effectiveness of the proposed control
algorithm is verified by simulation results. |
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