| 引用本文: | 林曼菲,张天平.具有执行器故障的不确定多智能体系统自适应动态面控制[J].控制理论与应用,2021,38(9):1452~1465.[点击复制] |
| LIN Man-fei,ZHANG Tian-ping.Adaptive dynamic surface control for uncertain multi-agent systems with actuator failures[J].Control Theory and Technology,2021,38(9):1452~1465.[点击复制] |
|
| 具有执行器故障的不确定多智能体系统自适应动态面控制 |
| Adaptive dynamic surface control for uncertain multi-agent systems with actuator failures |
| 摘要点击 1481 全文点击 522 投稿时间：2020-08-05 修订日期：2021-08-17 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| DOI编号 10.7641/CTA.2021.00512 |
| 2021,38(9):1452-1465 |
| 中文关键词 多智能体系统 执行器故障 输入量化 动态面控制 未建模动态 |
| 英文关键词 multi-agent systems actuator failure input quantification dynamic surface control unmodeled dynamics |
| 基金项目 国家自然科学基金项目(62073283), 江苏省自然科学基金项目(BK20181218), 扬州大学高端人才支持计划项目(2016) 资助. |
|
| 中文摘要 |
| 本文研究了一类具有输入量化、未建模动态和执行器故障的非线性多智能体系统的一致跟踪问题. 引入一
个可量测的动态信号消除未建模动态对系统的影响. 利用Young’s不等式和高斯函数的性质, 有效地处理了多智能
体邻居节点在设计的第一步中对子系统的耦合作用. 通过将滞回量化器表示为具有有界系数和有界扰动的输入线
性函数, 并利用动态面控制方法, 提出一种自适应神经网络动态面控制方案, 简化了控制器的设计, 保证了闭环系统
的所有信号都是半全局一致终结有界的, 所有跟随者都能实现期望的一致性. 最后, 仿真结果验证了所提出的自适
应控制策略的有效性. |
| 英文摘要 |
| In this paper, the problem of consensus tracking is studied for a class of nonlinear multi-agent systems with
input quantization and unmodeled dynamics as well as actuator failures. A measurable dynamic signal is introduced to
eliminate influence of unmodeled dynamics on the system. With the help of Young’s inequality and the characteristic
of Gaussian function, the interconnection of neighbors of multi-agent at the first step of controller design is effectively
handled. Using the linear function of input with bounded coefficient and bounded disturbance for the hysteresis quantizer
and dynamic surface control method, an adaptive dynamic surface control scheme is proposed, and the design of the
controller is simplified. It guarantees that all the signals of the closed-loop system are semi-globally uniformly ultimately
bounded (UUB), and all the followers can accomplish the desired consensus results. Finally, the simulation results verify
the availability of the proposed adaptive control strategy. |