Resilient Autonomous Control of Distributed Multi-agent Systems in Contested Environments
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An autonomous and resilient controller is proposed for leader-follower multi-agent systems under uncertainties and cyber-physical attacks. The leader is assumed non-autonomous with a nonzero control input which allows changing the team behavior or mission in response to environmental changes. A two-layer resilient learning-based control protocol is presented to find optimal solutions to the synchronization problem in the presence of attacks and system dynamic uncertainties. In the first security layer, an observer-based distributed H-infinity controller is designed to prevent propagating the effects of attacks on sensors and actuators throughout the network, as well as attenuate the effect of these attacks on the compromised agent itself. Non-homogeneous game algebraic Riccati equations are derived to solve the H-infinity optimal synchronization problem and an off-policy reinforcement learning is utilized to learn their solution without requiring any knowledge of the agent's dynamics. In the second security layer, a trust-confidence based distributed control protocol is proposed to mitigate attacks that hijack the entire node and attacks on communication links. A confidence value is defined for each agent based on only its local evidence. The proposed 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. Simulation results are provided to show the effectiveness of the proposed approach.
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