Event-triggered Cooperative Path Following of Multiple Autonomous Underwater Vehicles
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摘要: 针对考虑外部海洋环境扰动和内部模型不确定性的多自主水下航行器(Autonomous underwater vehicle, AUV), 研究其在通信资源受限和机载能量受限下的协同路径跟踪控制问题. 首先, 针对水声通信信道窄造成的通信资源受限问题, 设计一种基于事件触发机制(Event-triggered mechanism, ETM)的协同通信策略; 然后, 针对模型不确定性和海洋环境扰动问题, 设计一种基于事件触发机制的线性扩张状态观测器(Extended state observer, ESO)来逼近水下航行器的未知动力学, 并降低了系统采样次数; 最后, 针对机载能量受限问题, 设计一种基于事件触发机制的动力学控制律, 在保证控制精度的前提下, 降低了执行机构的动作频次, 从而节省了能量消耗. 应用级联系统稳定性分析方法, 分别验证了闭环系统是输入状态稳定的且系统不存在Zeno行为. 仿真结果验证了所提基于事件触发机制的多自主水下航行器协同路径跟踪控制方法的有效性.Abstract: A cooperative path following problem under limited communication resources and limited energy of multiple under-actuated autonomous underwater vehicles (AUV) subject to external marine environment disturbances and internal model uncertainty is considered. Firstly, a cooperative communication strategy based on an event-triggered mechanism (ETM) is proposed to solve the problem of limited communication resources caused by the narrow acoustic communication channel. Secondly, a linear extended state observer (ESO) based on an event-triggered mechanism is designed to deal with the model uncertainty and marine environment disturbance, and the observer is used to approximate the unknown dynamics of the underwater vehicles, such that the sampling times of the system could be reduced. Finally, a kinetic control law based on an event-triggered mechanism is designed to deal with the problem of limited energy, and this control law reduces the action times of the actuators while ensuring control accuracy, such that the energy consumption could be saved. It is proved that the closed loop system is input-to-state stable by using a cascade stability analysis, and Zeno behavior is excluded. The simulation results are given to demonstrate the effectiveness of the proposed event-triggered cooperative path following of multiple autonomous underwater vehicles.
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图 2 基于事件触发的协同路径跟踪控制器结构图
Fig. 2 Architecture of event-triggered cooperative path following controller
表 1 触发次数
Table 1 Triggering times
触发内容 事件触发 时间触发 采样
周期
(s)百分比
最大值
(%)AUV1 AUV2 AUV3 AUV$i$
($i$=1, 2, 3)协同次数($\chi_{i}$) 91 102 124 10 000 0.04 1.24 采样次数($u_{i}$) 324 352 420 10 000 0.04 4.20 采样次数($q_{i}$) 286 363 297 10 000 0.04 3.63 执行次数($\tau_{iu}$) 585 592 451 10 000 0.04 5.92 执行次数($\tau_{iq}$) 487 523 553 10 000 0.04 5.53 
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