Distributed adaptive tracking backstepping control in networked nonidentical Lagrange systems

Abstract

In this paper, the problem of adaptive tracking control in networked nonidentical Lagrange systems is investigated via backstepping schemes. Two distributed tracking control algorithms are designed for directed network topology graph with a spanning tree, where both the leader’s position and its velocity are assumed to be varying. Some generic criteria for adaptive tracking control algorithms with uncertain external disturbance and parametric uncertainties are presented. It is shown that the proposed algorithms only require a subset of the followers access to leader’s position. Furthermore, the adaptive control algorithm for uncertain external disturbance systems is robust, and it can avoid online measurement for neighbors’ velocities and efficiently eliminate the chattering during tracking. The results show that all followers can track the leader’s dynamics. Two examples and their simulations show the effectiveness of the proposed algorithms.