Abstract
In this study, mathematical models of hierarchical multi-agent systems (HMSs) are first proposed to demonstrate the hierarchical structure of multi-agent systems. Communication disturbance is also considered in HMSs since disturbance often appears when information is transmitted among agents due to various uncertainties such as model unsteadiness and external noise. Radial basis function neural networks are applied to approximate the non-linear functions of the communication disturbance. Then, by applying a backstepping method based on the hierarchical structure of HMSs, a simple condition is derived to ensure the stability of HMSs with disturbance.
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