Resilient Autonomous Control of Distributed Multi-agent Systems in Contested Environments

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 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_infinity 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. Non-homogeneous game algebraic Riccati equations are derived to solve the H_infinity optimal synchronization problem and off-policy reinforcement learning 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 reinforcement learning 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

Event-triggered output consensus for linear multi-agent systems via adaptive distributed observer

summary:This paper investigates the distributed event-triggered cooperative output regulation problem for heterogeneous linear continuous-time multi-agent systems (MASs). To eliminate the requirement of continuous communication among interacting following agents, an event-triggered adaptive distributed observer is skillfully devised. Furthermore, a class of closed-loop estimators is constructed and implemented on each agent such that the triggering times on each agent can be significantly reduced while at the same time the desired control performance can be preserved. Compared with the existing open-loop estimators, the proposed estimators can provide more accurate state estimates during each triggering period. It is further shown that the concerned cooperative output regulation problem can be effectively resolved under the proposed control scheme and the undesirable Zeno behavior can be excluded. Finally, the effectiveness of the proposed results is verified by numerical simulations

Fully Distributed Adaptive Controllers for Cooperative Output Regulation of Heterogeneous Linear Multi-agent Systems with Directed Graphs

This paper considers the cooperative output regulation problem for linear multi-agent systems with a directed communication graph, heterogeneous linear subsystems, and an exosystem whose output is available to only a subset of subsystems. Both the cases with nominal and uncertain linear subsystems are studied. For the case with nominal linear subsystems, a distributed adaptive observer-based controller is designed, where the distributed adaptive observer is implemented for the subsystems to estimate the exogenous signal. For the case with uncertain linear subsystems, the proposed distributed observer and the internal model principle are combined to solve the robust cooperative output regulation problem. Compared with the existing works, one main contribution of this paper is that the proposed control schemes can be designed and implemented by each subsystem in a fully distributed fashion for general directed graphs. For the special case with undirected graphs, a distributed output feedback control law is further presented.Comment: 8 pages, 2 figures. submitted for publicatio