Hierarchical Event-Triggered Predictive Control for Cross-Domain Unmanned Systems with Mixed Constraints

IF 19.2 1区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-10-20 DOI:10.1109/JAS.2024.124797

Ming-Feng Ge;Yi-Fan Li;Chen-Bin Wu;Zhi-Wei Liu;Yan Jia;Si-Sheng Liu

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Abstract

Dear Editor, This letter investigates the problem of multi-dimension formation tracking (MDFT) for the cross-domain unmanned systems, including several interconnected agents, namely, unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs). We assume that each agent suffers from by the mixed constraints on its velocity, control input and Euler angle. Solving the MDFT problem implies that 1) The virtual state of each USV is determined in the earth coordinate by expanding its 2D work space to the 3D space; 2) The UAVs and the virtual states of the USVs form a user-defined geometric formation asymptotically in the 3D local coordinate; 3) The geometric center of the UAVs and the virtual states of the USVs tracks a reference trajectory asymptotically in the 3D earth coordinate. Therefore, a new hierarchical event-triggered predictive control (HETPC) algorithm is proposed to solve the MDFT problem, including the event-triggere cooperation layer and local 1ayer. The former solves the cooperative estimation problem of cross-domain systems with different dimensions, and the latter solves the trajectory tracking control problem under mixed constraints.

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混合约束下跨域无人系统的层次事件触发预测控制

这封信研究了跨域无人系统的多维编队跟踪（MDFT）问题，包括几个相互关联的代理，即无人飞行器（uav）和无人水面飞行器（usv）。我们假设每个智能体都受到速度、控制输入和欧拉角的混合约束。解决MDFT问题意味着：1)将每个USV的二维工作空间扩展到三维空间，在地球坐标中确定其虚拟状态；2)无人机与usv的虚拟状态在三维局部坐标上渐近形成用户自定义的几何编队；3)无人机的几何中心和无人机的虚拟状态在三维地球坐标中渐近地跟踪一个参考轨迹。为此，提出了一种新的分层事件触发预测控制（HETPC）算法来解决MDFT问题，该算法包括事件触发协作层和局部层。前者解决了不同维数的跨域系统的协同估计问题，后者解决了混合约束下的轨迹跟踪控制问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ieee-Caa Journal of Automatica Sinica Engineering-Control and Systems Engineering

CiteScore

23.50

自引率

11.00%

发文量

880

期刊介绍： The IEEE/CAA Journal of Automatica Sinica is a reputable journal that publishes high-quality papers in English on original theoretical/experimental research and development in the field of automation. The journal covers a wide range of topics including automatic control, artificial intelligence and intelligent control, systems theory and engineering, pattern recognition and intelligent systems, automation engineering and applications, information processing and information systems, network-based automation, robotics, sensing and measurement, and navigation, guidance, and control. Additionally, the journal is abstracted/indexed in several prominent databases including SCIE (Science Citation Index Expanded), EI (Engineering Index), Inspec, Scopus, SCImago, DBLP, CNKI (China National Knowledge Infrastructure), CSCD (Chinese Science Citation Database), and IEEE Xplore.