UAV group control protocol with adaptive consensus

IF 3.9 4区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Dmytro P. Kucherov, Guodong Jiang, Huaqing Liu, Minglei Fu
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引用次数: 0

Abstract

In this paper, a modified consensus protocol algorithm is proposed for controlling a group of identical unmanned aerial vehicles (UAVs), which have been subjected to interfering signals during coordinate information exchange, using an unknown parameter in the consensus protocol. The maximum levels of interfering signals in the proposed protocol were adjusted by incorporating a hysteresis function with a dead zone consistent with the initial coordinates and the interfering signal levels. An adaptation algorithm is proposed to address a priori uncertainty regarding the consensus parameters, involving the correction of an unknown parameter by eliminating control signals exhibiting false sign changes. This correction relies on the coordinates in the phase plane, indicating that the delay in maneuver execution occurs at the beginning of the maneuver. Furthermore, by modeling synchronized motion, UAV group consensus is demonstrated for an ideal case devoid of a priori uncertainty regarding control protocol parameters, interfering signals, or consequences. The convergence of the adaptation algorithm was assessed by defining a vector function to track parameter changes during tuning. The monotonically decreasing nature of the resulting curve, along with the finite duration of the tuning process, provides confirmation of the convergence of the adaptation algorithm.

具有自适应共识的无人机群控协议
摘要本文提出了一种改进的共识协议算法,用于控制一组相同的无人驾驶飞行器(UAV),这些飞行器在坐标信息交换过程中受到干扰信号的影响,共识协议中使用了一个未知参数。拟议协议中干扰信号的最大水平是通过加入一个滞后函数来调整的,该滞后函数的死区与初始坐标和干扰信号水平一致。为解决共识参数的先验不确定性,提出了一种适应算法,包括通过消除显示假信号变化的控制信号来修正未知参数。这种修正依赖于相位平面上的坐标,表明机动执行的延迟发生在机动开始时。此外,通过同步运动建模,无人飞行器群组共识在理想情况下得到了验证,即在控制协议参数、干扰信号或后果方面不存在先验不确定性。通过定义一个向量函数来跟踪调整过程中的参数变化,对适应算法的收敛性进行了评估。结果曲线的单调递减性质以及调整过程的有限持续时间证实了适应算法的收敛性。
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来源期刊
CiteScore
5.30
自引率
16.10%
发文量
163
审稿时长
5 months
期刊介绍: The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material. Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include: Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers Nonlinear, Robust and Intelligent Adaptive Controllers Linear and Nonlinear Multivariable System Identification and Estimation Identification of Linear Parameter Varying, Distributed and Hybrid Systems Multiple Model Adaptive Control Adaptive Signal processing Theory and Algorithms Adaptation in Multi-Agent Systems Condition Monitoring Systems Fault Detection and Isolation Methods Fault Detection and Isolation Methods Fault-Tolerant Control (system supervision and diagnosis) Learning Systems and Adaptive Modelling Real Time Algorithms for Adaptive Signal Processing and Control Adaptive Signal Processing and Control Applications Adaptive Cloud Architectures and Networking Adaptive Mechanisms for Internet of Things Adaptive Sliding Mode Control.
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