Adaptive Fault Tolerant Neural Control of Heterogeneous Second-Order Multi-Agent Systems

This paper addresses the stationary average consensus problem for second-order nonlinear multi-agent systems with heterogeneous unknown masses and uncertain nonlinear functions under balanced directed networks. Taking advantage of radial basis function neural networks, the unknown nonlinearity existed in the dynamics is counteracted. To deal with the unknown inertias, external disturbances, and actuator faults, adaptive control and sliding mode technique are utilized. By the theoretical analysis, it is shown that average consensus is reached provided that the network topology is balanced and contains a spanning tree. The proposed framework also guarantees consensus with the condition that the underlying network topology has a directed spanning tree. Finally, simulation results are given to better highlight the validity of the theoretical results.