Consensus Tracking Control for Distributed Nonlinear Multiagent Systems via Adaptive Neural Backstepping Approach

Abstract

This paper aims to address adaptive tracking control problem of distributed multiagent systems. Differing from some existing works, each follower under consideration is modeled by a nonlinear nonstrict feedback system, especially, the virtual and real control gains are unknown functions rather than constants. To overcome the difficulty caused by the unknown nonlinearities, radial basis function neural networks are employed to model those unknown nonlinearities. Then, adaptive neural approach and backstepping technique are combined to construct the consensus tracking control protocol. It is shown that under the action of the suggested control protocol, whole closed-loop system is stable and all the outputs of followers ultimately track the reference signal, i.e., the output of the leader, synchronously. Numerical simulation is presented to further demonstrate the efficacy of the suggested control proposal.