Resilient Autonomous Control of Distributed Multiagent Systems in Contested Environments
Abstract
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.
Recommended Citation
R. Moghadam and H. Modares, "Resilient Autonomous Control of Distributed Multiagent Systems in Contested Environments," IEEE Transactions on Cybernetics, vol. 49, no. 11, pp. 3957 - 3967, Institute of Electrical and Electronics Engineers (IEEE), Nov 2019.
The definitive version is available at https://doi.org/10.1109/TCYB.2018.2856089
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Autonomous Controller; Distributed Control; H∞; Control; Multiagent System (MAS); Reinforcement Learning (RL); Resilient Controller
International Standard Serial Number (ISSN)
2168-2267
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Nov 2019
