Distributed Multiagent Systems

This chapter focuses on two directions in discrete-time multiagent systems. The first direction deals with leader-following and leaderless consensus problems and the second direction provides a systematic analysis of the adverse effects of cyber-physical attacks. Along the first direction, a distributed observer-based consensus protocol is proposed to investigate the consensus problem for multiagent systems of general discrete-time linear dynamics. By means of the observer, the distributed control law of each agent is designed using local information to guarantee consensus, and the corresponding sufficient conditions are obtained by exploiting the graph and control theory approach. A modified distributed event-triggered consensus protocol is designed to reduce communication congestion. Detailed analysis of the leaderless and the leader-following consensus is presented for both observer-based and full-information protocols. The second direction gives an analysis of the adverse effects of cyber-physical attacks on discrete-time distributed multiagent systems, and develops a mitigation approach for attacks on sensors and actuators. First, we show how an attack on a compromised agent can propagate and affect intact agents that are reachable from it. That is, an attack on a single node snowballs into a network-wide attack and can even destabilize the entire system. Moreover, we show that the attacker can bypass the robust H_∞ control protocol and make it entirely ineffective in attenuating the effect of the adversarial input on the system performance. Finally, to overcome the adversarial effects of attacks on sensors and actuators, a distributed adaptive attack compensator is designed by estimating the normal expected behavior of agents. The adaptive attack compensator is augmented with the controller, and it is shown that the proposed controller achieves secure consensus in the presence of the attacks on sensors and actuators. This controller does not require to make any restrictive assumption on the number of agents or agent’s neighbors under the direct effect of adversarial input. Moreover, it recovers compromised agents under actuator attacks and avoids propagation of attacks on sensors without removing compromised agents. Simulation examples are provided to demonstrate the effectiveness and capabilities of the established theories under different scenarios.