Robust Event-triggered Distributed Average Tracking for Double-integrator Agents Without Velocity Measurements
Robust Event-triggered Distributed Average Tracking for Double-integrator Agents Without Velocity Measurements
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DOI：
10.1109/cdc42340.2020.9303975
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Yong Ding;W. Ren;Yu Zhao
中科院分区：
文献类型：
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作者：
Yong Ding;W. Ren;Yu Zhao
本文研究了一组双积分智能体的分布式平均跟踪问题。在一些实际应用中，由于技术和空间限制，速度测量可能无法实现，而且通常精度较低且实施成本较高。为此，建立了一种不使用速度测量和正确初始化的分布式平均跟踪算法。值得注意的是，参数设计不需要全局信息。然后，为了消除连续交互的要求，降低通信成本并提高能量效率，通过结合事件触发通信策略而不使用速度测量，设计了事件触发分布式平均跟踪算法。准确地说，采用动态事件触发策略的思想，为每个智能体构造一个触发条件，以保证排除Zeno行为。最后，提供模拟来说明所获得的结果。
This paper investigates the distributed average tracking problem for a group of double-integrator agents. In some practical applications, velocity measurements may be unavailable due to technology and space limitations, and it is also usually less accurate and more expensive to implement. To this end, a distributed average tracking algorithm without using velocity measurements and correct initialization is established. It is worth noting that no global information is needed for parameter design. Then, in order to remove the requirement on continuous interaction, and reduce the communication cost and improve the energy efficiency, an event-triggered distributed average tracking algorithm is designed by incorporating an event-triggered communication strategy without using velocity measurements. To be exact, the idea of a dynamic event-triggered strategy is used to construct a triggering condition for each agent to guarantee the exclusion of Zeno behavior. Finally, simulations are provided to illustrate the obtained results.