Fixed-Time Optimal Fault-Tolerant Formation Control With Prescribed Performance for Fixed-Wing UAVs Under Dual Faults

IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Bo Meng;Ke Zhang;Bin Jiang
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引用次数: 0

Abstract

This article aims to propose a novel fixed-time distributed optimized formation control scheme for fixed-wing unmanned aerial vehicles with uncertainties, communication link and actuator faults, and performance constraint. Firstly, the prescribed performance function is introduced to improve the steady-state and transient performances of fixed-wing UAVs system. Communication link faults are tolerated by utilizing the distributed leader state observer. Subsequently, with the objective of establishing optimal controllers for velocity and altitude subsystems, the reinforcement learning control method is employed. Simultaneously, an intermediate controller is constructed to tackle the difficulties in applying reinforcement learning to the fault-tolerant control scheme. In addition, new adaptive laws of fault factor parameters are proposed, which can make the fault-tolerant scheme align better with the concept of fixed-time convergence. Finally, fixed-time prescribed performance controllers for velocity and altitude subsystems are developed. The designed control algorithm can ensure that the velocity and altitude tracking errors converge to the prescribed region, and the simulation results further demonstrate that the proposed control strategy is effective.
双故障条件下具有规定性能的固定翼无人机固定时间最优容错编队控制
本文旨在为具有不确定性、通信链路和作动器故障以及性能约束的固定翼无人飞行器提出一种新型固定时间分布式优化编队控制方案。首先,引入规定性能函数来改善固定翼无人飞行器系统的稳态和瞬态性能。利用分布式领导者状态观测器容忍通信链路故障。随后,以建立速度和高度子系统的最优控制器为目标,采用了强化学习控制方法。同时,还构建了一个中间控制器,以解决将强化学习应用于容错控制方案的困难。此外,还提出了新的故障因子参数自适应规律,使容错方案更好地符合固定时间收敛的概念。最后,还为速度和高度子系统开发了固定时间规定性能控制器。所设计的控制算法能确保速度和高度跟踪误差收敛到规定区域,仿真结果进一步证明了所提出的控制策略是有效的。
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 Transactions on Signal and Information Processing over Networks
IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications
CiteScore
5.80
自引率
12.50%
发文量
56
期刊介绍: The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.
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