基于随机有向图的线性多Agent系统抗恶意攻击的分布式凸优化
Attack-Resilient Distributed Convex Optimization of Linear Multi-Agent Systems Against Malicious Cyber-Attacks over Random Digraphs
摘要：
研究了随机有向图上异构线性多智能体系统在拒绝服务攻击下的弹性指数分布凸优化问题.随机有向图是由不可靠的网络引起的，而允许非周期性发生的DoS攻击是指智能对手中断通信信道。与现有的许多分布式凸优化算法相比，在网络环境下，由于网络环境的不利影响，可能无法得到全局最优解，从而导致优化算法性能下降甚至失效。上述设置对优化算法设计和指数收敛分析提出了某些技术挑战。在这项工作中，提出了几个弹性算法，使一个团队的代理最小化的总和，当地的非二次费用函数在一个安全可靠的方式与全球指数收敛。受[15]-[18]中的初步工作的启发，研究了对攻击频率和持续时间的显式分析，以保证指数最优解。数值模拟结果证明了所提出的设计的有效性。
英文摘要：
This paper addresses a resilient exponential distributed convex optimization problem for a heterogeneous linear multi-agent system under Denial-of-Service (DoS) attacks over random digraphs. The random digraphs are caused by unreliable networks and the DoS attacks, allowed to occur aperiodically, refer to an interruption of the communication channels carried out by the intelligent adversaries. In contrast to many existing distributed convex optimization works over a prefect communication network, the global optimal solution might not be sought under the adverse influences that result in performance degradations or even failures of optimization algorithms. The aforementioned setting poses certain technical challenges to optimization algorithm design and exponential convergence analysis. In this work, several resilient algorithms are presented such that a team of agents minimizes a sum of local non-quadratic cost functions in a safe and reliable manner with global exponential convergence. Inspired by the preliminary works in [15]-[18], an explicit analysis of frequency and duration of attacks is investigated to guarantee exponential optimal solutions. Numerical simulation results are presented to demonstrate the effectiveness of the proposed design.
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