Attack-Defense Game of Heterogeneous Multi-Agent Systems Under Actuator Faults

IF 6.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Yadong Li;Bin Hu;Tao Li;Zhi-Hong Guan
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引用次数: 0

Abstract

This paper investigates N versus N attack-defense game problem of heterogeneous nonlinear multi-agent systems under the condition of actuator faults on the defensive side. Specifically, N attackers need to approach the target area as closely as possible in order to attack it, while N defenders need to approach the attackers as closely as possible and intercept them. We decompose this problem into two N-player nonzero-sum game problems. Based on differential game method, the optimal attacking game strategies for the attackers are first presented. Then, considering that the defenders contains actuator faults, this paper designs a fault observer for each defending agent based on adaptive estimation theory to estimate unknown actuator fault and proposes a novel optimal defense game strategy applicable for actuator fault. To address the problem that it is difficult to directly solve the optimal control strategies in nonlinear systems, we propose an online learning algorithm based on adaptive dynamic programming to obtain approximate solutions for the optimal cost functions and the optimal control strategies. The effectiveness of the proposed method is verified through theoretical analysis and simulation examples. Note to Practitioners—The problem of attack-defense confrontation for multi-agent systems has a broad practical background, such as resource competition, missile interception, and base defense, etc. By introducing game theory, complex attack-defense confrontation problems for multi-agent systems can be clearly formulated. However, most studies on attack-defense game confrontation are still limited to a small number of intelligent agents. Additionally, actuator faults with frequent and great harm pose significant challenges to the safe and stable operation of multi-agent systems in complex and changeable practical applications. Especially in the attack-defense confrontation scenario, actuator faults may be caused not only by the system’s own physical faults but also by the malicious attacks from attackers. Based on the above considerations, this paper studies the N versus N attack-defense game problem of heterogeneous nonlinear multi-agent systems when the defenders have actuator faults. The proposed control scheme not only ensures the smooth implementation of interception tasks for the defenders with actuator faults, but also significantly saves time and control input energy compared with the traditional formation tracking control method without actuator faults. It is hoped that this paper can provide some inspiration for the attack-defense confrontation problem of multi-agent systems under actuator faults.
执行器故障下异构多智能体系统的攻防对策
研究了异构非线性多智能体系统在防御端执行器故障情况下的N对N攻防对策问题。具体来说,N个攻击者需要尽可能靠近目标区域进行攻击,而N个防御者需要尽可能靠近攻击者并进行拦截。我们把这个问题分解成两个n人的非零和博弈问题。基于微分对策方法,提出了攻击者的最优攻击对策。然后,考虑到防御体中包含执行器故障,基于自适应估计理论为每个防御体设计一个故障观测器来估计未知的执行器故障,提出了一种适用于执行器故障的新型最优防御博弈策略。为了解决非线性系统难以直接求解最优控制策略的问题,提出了一种基于自适应动态规划的在线学习算法,以获得最优代价函数和最优控制策略的近似解。通过理论分析和仿真算例验证了该方法的有效性。从业者须知——多智能体系统的攻防对抗问题具有广泛的实践背景,如资源竞争、导弹拦截、基地防御等。通过引入博弈论,可以清晰地表述多智能体系统的复杂攻防对抗问题。然而,大多数关于攻防博弈对抗的研究仍然局限于少数智能体。此外,在复杂多变的实际应用中,执行机构故障频繁且危害大,对多智能体系统的安全稳定运行提出了重大挑战。特别是在攻防对抗场景下,执行器故障不仅可能是由于系统自身的物理故障,也可能是由于攻击者的恶意攻击。基于以上考虑,本文研究了防御方存在执行器故障时异构非线性多智能体系统的N对N攻防博弈问题。所提出的控制方案不仅保证了执行器故障防御者拦截任务的顺利执行,而且与传统的无执行器故障编队跟踪控制方法相比,显著节省了时间和控制输入能量。希望本文能为多智能体系统在执行器故障情况下的攻防对抗问题提供一些启示。
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 Automation Science and Engineering
IEEE Transactions on Automation Science and Engineering 工程技术-自动化与控制系统
CiteScore
12.50
自引率
14.30%
发文量
404
审稿时长
3.0 months
期刊介绍: The IEEE Transactions on Automation Science and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. T-ASE welcomes results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, security, service, supply chains, and transportation. T-ASE addresses a research community willing to integrate knowledge across disciplines and industries. For this purpose, each paper includes a Note to Practitioners that summarizes how its results can be applied or how they might be extended to apply in practice.
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