Observer‐based consensus tracking control for a class of nonstrict‐feedback nonlinear multi‐agent systems with prescribed performance and input quantization

Abstract This article investigates the consensus tracking problem with predefined transient and steady performance requirements for a class of nonstrict‐feedback nonlinear multi‐agent systems (MASs) with input quantization under a directed graph. Based on prescribed performance error transformation methods and command filtered backstepping techniques, a novel observer‐based adaptive control protocol is proposed, where neural observers are designed to estimate unmeasurable states and radial basis function neural networks are constructed to compensate command filter errors. The proposed protocol can be applied to a more general class of nonlinear MASs with nonstrict‐feedback nonlinear dynamics and unmeasurable states information. It is strictly proved that all signals in the whole MAS are semi‐globally uniformly ultimately bounded and both the transient and steady performances of the consensus tracking errors satisfy prescribed performance requirements. Finally, three numerical examples are presented to validate the effectiveness of the proposed protocol.