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Abstract Resilience of multi-agent systems (MAS) reflects their capability to maintain normal operation, at a prescribed level in the presence of unintended faults. In this paper, we investigate resilient control of MAS under faults on sensors and actuators. We propose four resilient state feedback based leader–follower tracking protocols. For the case of sensor faults, we develop an adaptive compensation protocol and an H ∞ control protocol. For the case of simultaneous sensor and actuator faults, we further propose an enhanced adaptive compensation protocol and an enhanced H ∞ control protocol. We show the duality between the adaptive compensation protocols and the H ∞ control protocols. For adaptive compensation protocols, faults on sensors and actuators are rejected by using local adaptive sensor and actuator compensators, respectively. Moreover, by employing a static output-feedback design technique, we propose H ∞ control protocols that guarantee bounded L 2 gains of certain errors in terms of the L 2 norms of fault signals. This further allows us to prove resilience even if sensor faults are unbounded. Finally, simulation studies validate the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]

This work presents an output-feedback policy learning algorithm underlining input–output system data for distributed robust optimal synchronization of heterogeneous multi-agent systems. The output-feedback synchronization problem in the context of this work is formulated via robust output regulation and reinforcement learning modeling the interactions among agents by a zero-sum game. The proposed learning and control structure only requires the local system data for each agent and distributed output data among communicating neighbors. We utilize system-level synchysis for the continuous-time state reconstruction for the distributed learning with convergence and stability proofs under the proposed output-feedback policy for solving the zero-sum game. We further show that policy learning is assured under the proposed data criteria relating to input–output data only rather than any inter-immediate gains from policy iterations. Based on the cooperative robust output regulation, this work gains robustness after the learning is complete and establishes an output data-driven distributed optimal robust synchronization without knowing accurate system dynamics. A numerical example shows the effectiveness of the proposed learning algorithm. [ABSTRACT FROM AUTHOR]

In this work, an event-triggered coordination problem for multi-agent systems with general linear dynamics is considered over directed communication graphs with a spanning tree. We study such a problem by using a Lyapunov equation for leaderless consensus. It is shown that a Lyapunov-based approach can complete stability analysis of event-triggered consensus of multi-agent systems over general directed graphs. [ABSTRACT FROM AUTHOR]

The resilience of multi-agent systems (MAS) measures the capability of recovering the cooperative performance after experiencing faults or uncertainties. In this paper, we seek the improvement of the synchronization resilience for MAS by means of addressing unknown faults on communication links. To this end, we first transform the resilient control problem into designing distributed state observers. In addition, we show that the proposed state observers are resilient to communication link faults. Finally, we provide the main result of this paper as a synchronization protocol for MAS even in the presence of communication link faults. [ABSTRACT FROM AUTHOR]