Distributed adaptive output feedback tracking control for a class of uncertain nonlinear multi-agent systems

ABSTRACT

This paper addresses the distributed output feedback tracking control problem for multi-agent systems with higher order nonlinear non-strict-feedback dynamics and directed communication graphs. The existing works usually design a distributed consensus controller using all the states of each agent, which are often immeasurable, especially in nonlinear systems. In this paper, based only on the relative output between itself and its neighbours, a distributed adaptive consensus control law is proposed for each agent using the backstepping technique and approximation technique of Fourier series (FS) to solve the output feedback tracking control problem of multi-agent systems. The FS structure is taken not only for tracking the unknown nonlinear dynamics but also the unknown derivatives of virtual controllers in the controller design procedure, which can therefore prevent virtual controllers from containing uncertain terms. The projection algorithm is applied to ensure that the estimated parameters remain in some known bounded sets. Lyapunov stability analysis shows that the proposed control law can guarantee that the output of each agent synchronises to the leader with bounded residual errors and that all the signals in the closed-loop system are uniformly ultimately bounded. Simulation results have verified the performance and feasibility of the proposed distributed adaptive control strategy.

KEYWORDS:

• Output consensus
• distributed control
• output feedback
• non-strict-feedback systems
• adaptive control

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. ab ≤ a^p/p + b^q/q where a, b ≥ 0 and p, q > 0 such that 1/p + 1/q = 1.

2. (∑^p_{k = 1}a_kb_k)² ⩽ (∑^p_{k = 1}a²_k)(∑_{k = 1}^pb²_k).

Additional information

Funding

This paper was partially supported by the Scientific Innovation program [grant number 13ZZ115]; the National Natural Science Foundation [grant number 61374040], [grant number 61203143]; Hujiang Foundation of China [grant number C14002]; Graduate Innovation program of Shanghai grant number 54-13-302-102]; the Natural Science Foundation of Jiangsu [grant number BK20140640].