Adaptive guaranteed-performance consensus design for high-order multiagent systems

Fixed-time consensus of nonlinear multi-agent system with uncertain disturbances based on event-triggered strategy

This paper presents a fixed-time consensus problem for multi-agent systems (MASs) with nonlinear dynamics and uncertain disturbances, the event-triggered strategy is proposed to address this issue. Firstly, based on event-triggered control theory, a fixed-time consensus protocol is proposed, which can avoid continuous communication and greatly reduces the power consumption of proposed protocols. Secondly, based on graph topology, fixed-time control method and Lyapunov theory, the novel fixed-time consensus criteria of MASs with nonlinearity under Lipschitz condition and uncertain disturbances bounds is derived via the designed protocols. Furthermore, the consensus can be guaranteed by setting the proper parameters, it should be pointed out that for any given initial state, the MASs can reach consensus within the setting time. Thirdly, by utilizing the above control strategy, the MASs have no Zeno behavior. Finally, two simulation examples and the practical application of unmanned air vehicles (UAVs) verify the reliability of proposed control scheme.

Supportive consensus

The paper is concerned with the consensus problem in a multi-agent system such that each agent has boundary constraints. Classical Olfati-Saber’s consensus algorithm converges to the same value of the consensus variable, and all the agents reach the same value. These algorithms find an equality solution. However, what happens when this equality solution is out of the range of some of the agents? In this case, this solution is not adequate for the proposed problem. In this paper, we propose a new kind of algorithms called supportive consensus where some agents of the network can compensate for the lack of capacity of other agents to reach the average value, and so obtain an acceptable solution for the proposed problem. Supportive consensus finds an equity solution. In the rest of the paper, we define the supportive consensus, analyze and demonstrate the network’s capacity to compensate out of boundaries agents, propose different supportive consensus algorithms, and finally, provide some simulations to show the performance of the proposed algorithms.