An autonomous and resilient controller is proposed for leader-follower multiagent systems under uncertainties and cyber-physical attacks. The leader is assumed nonautonomous with a nonzero control input, which allows changing the team behavior or mission in response to the environmental changes. A resilient learning-based control protocol is presented to find optimal solutions to the synchronization problem in the presence of attacks and system dynamic uncertainties. An observer-based distributed H∞ controller is first designed to prevent propagating the effects of attacks on sensors and actuators throughout the network, as well as to attenuate the effect of these attacks on the compromised agent itself. Nonhomogeneous game algebraic Riccati equations are derived to solve the H∞ optimal synchronization problem and off-policy reinforcement learning (RL) is utilized to learn their solution without requiring any knowledge of the agent's dynamics. A trust-confidence-based distributed control protocol is then proposed to mitigate attacks that hijack the entire node and attacks on communication links. A confidence value is defined for each agent based solely on its local evidence. The proposed resilient RL algorithm employs the confidence value of each agent to indicate the trustworthiness of its own information and broadcast it to its neighbors to put weights on the data they receive from it during and after learning. If the confidence value of an agent is low, it employs a trust mechanism to identify compromised agents and remove the data it receives from them from the learning process. The simulation results are provided to show the effectiveness of the proposed approach.

中文翻译：

---

竞争环境中分布式多智能体系统的弹性自主控制


为不确定性和网络物理攻击下的领导者-跟随者多智能体系统提出了一种自主且有弹性的控制器。假设领导者具有非零控制输入，是非自主的，这允许改变团队行为或任务以响应环境变化。提出了一种基于弹性学习的控制协议，以在存在攻击和系统动态不确定性的情况下找到同步问题的最佳解决方案。基于观察器的分布式 H∞ 控制器首先设计用于防止在整个网络中传播对传感器和执行器的攻击影响，并减弱这些攻击对受感染代理本身的影响。导出非齐次博弈代数 Riccati 方程来解决 H∞ 最优同步问题，并利用离策略强化学习 (RL) 来学习其解决方案，而无需了解代理的动态。然后提出了一种基于信任-置信度的分布式控制协议来减轻劫持整个节点的攻击和对通信链路的攻击。仅根据其本地证据为每个代理定义置信值。所提出的弹性强化学习算法利用每个代理的置信度值来指示其自身信息的可信度，并将其广播给其邻居，以对学习期间和学习后从其收到的数据进行加权。如果代理的置信值较低，它会采用信任机制来识别受损的代理，并从学习过程中删除从代理收到的数据。仿真结果显示了所提出方法的有效性。