基于 Lyapunov 矩阵的自适应弹性控制,用于具有传感器和推进器攻击的无人驾驶海洋飞行器。

IF 6.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Xin Yang , Li-Ying Hao , Yang Xiao , Tieshan Li
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引用次数: 0

摘要

本文介绍了在与状态相关的传感器攻击、与输入相关的推进器攻击和时间延迟的情况下,为无人海洋航行器(UMV)设计基于 Lyapunov 矩阵的自适应弹性控制器。与依赖于状态信息的推进器攻击模型不同,本文研究的推进器攻击模型与控制输入有关,即依赖于输入的推进器攻击。这意味着设计的修正信号也会受到攻击的影响。为了减轻所考虑的传感器攻击和推进器攻击对 UMV 的影响,本文采用了一种自适应机制来估计攻击因子。此外,还引入了基于李亚普诺夫矩阵的完全型李亚普诺夫-克拉索夫斯基函数(LKF),其中考虑了更全面的时延信息。在此基础上,利用线性矩阵不等式(LMI)方法和詹森不等式获得了控制器存在的充分条件。提出的控制器保证了 UMV 的状态误差渐近收敛为零,自适应 H∞ 性能指标不大于 γ0。最后,仿真结果验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lyapunov matrix-based adaptive resilient control for unmanned marine vehicles with sensor and thruster attacks

This paper presents the design of a Lyapunov matrix-based adaptive resilient controller for unmanned marine vehicles (UMVs) under state-dependent sensor attacks, input-dependent thruster attacks, and time delays. Different from the thruster attack model that depends on state information, the thruster attack model studied in this paper is related to control input, that is, the input-dependent thruster attacks. This implies that the designed correction signal is also affected by the attacks. To mitigate the impact of the considered sensor attacks and thruster attacks on UMVs, an adaptive mechanism is employed to estimate the attack factors. Furthermore, a Lyapunov matrix-based complete-type Lyapunov–Krasovskii functional (LKF) is introduced, in which more comprehensive time delay information are considered. Based on this, linear matrix inequality (LMI) method and Jensen’s inequality are used to obtain sufficient conditions for the existence of the controller. The proposed controller guarantees that the state errors of UMVs converge asymptotically to zero with the adaptive H performance index no larger than γ0. Finally, the efficacy of the proposed approach is verified by simulation results.

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来源期刊
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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