Fully Distributed Consensus of Switched Heterogeneous Nonlinear Multi-Agent Systems With Bouc-Wen Hysteresis Input

This paper studies the adaptive fully distributed consensus problem for a class of nonlinear multi-agent systems (MASs) whose structures are heterogeneous, where agents with first-order dynamics and second-order dynamics exist simultaneously. Moreover, each agent subjects to Bouc-Wen hysteresis input. Due to the changes of the internal and external environment, all agents are considered to possess multiple modes and display the switching characteristic. To achieve the consensus tracking control of the considered MAS, first, the totally unknown nonlinear functions representing the system uncertainties are approximated by utilizing radial basis function neural networks (RBFNNs). Then, an adaptive neural consensus control protocol, which can enable the consensus objective to be achieved for the MAS, is designed via using the fully distributed design scheme and adaptive neural control method. It is noted that the information on the number of agents and the interaction topology is not required during the protocol design. Based on the Lyapunov stability theory, the proposed consensus protocol can ensure all signals of the considered MAS is bounded. Finally, simulation results are presented to show the feasibility and effectiveness of the proposed consensus protocol.

Automated summary

This paper investigates the adaptive fully distributed consensus problem for a class of nonlinear multi-agent systems with heterogeneous structures and hysteresis inputs, achieving consensus tracking control by approximating system uncertainties with neural networks and designing an adaptive neural consensus control protocol. The proposed protocol ensures bounded signals of the system through Lyapunov stability theory, without requiring information on the number of agents and interaction topology during design.