基于估计量的多aav双模型保连性预测控制

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-01-22 DOI:10.1109/TNSE.2025.3532475

Zhixu Du;Hao Zhang;Zhuping Wang;Huaicheng Yan

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引用次数: 0

摘要

研究了多自主飞行器（aav）之间保持连通性和避免碰撞的分布式控制问题。提出了一种新的aav分布式估计器。下面的aav利用邻居的信息来估计所有aav的输出信息。通过结合连通性维护功能和无碰撞势场功能，以下自动驾驶汽车在保持网络连通性的同时避免了相互碰撞和障碍物。为了快速跟踪领先的AAV，设计了一种双模型预测控制（dual-MPC）算法，即外环和内环模型预测控制优化。将滚动优化与模糊逻辑系统相结合，保证了双mpc算法的稳定性和可行性。最后，仿真结果验证了所提控制器的有效性。

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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Estimator-Based Dual-Model Predictive Control for Multi-AAVs With Connectivity-Preserving

This paper investigates a distributed control problem for maintaining connectivity and avoiding collisions among multiple autonomous aerial vehicles (AAVs). A novel distributed estimator is proposed for AAVs. The following AAVs utilize information from their neighbors to estimate the output information of all AAVs. By incorporating a connectivity maintenance function and a collision-free potential field function, the following AAVs avoid collisions with each other and obstacles while maintaining network connectivity. A dual-model predictive control (dual-MPC) algorithm for AAVs, referred to as outer-loop and inner-loop model predictive control optimization, is designed to quickly track the leading AAV. Stability and feasibility of the dual-MPC algorithm can be ensured by uniting rolling optimization with fuzzy logic systems. Finally, the simulation results confirm the effectiveness of the proposed controller.

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.