Neural network-based distributed intelligent supervisory control for multi-agent systems with unknown input powers

Abstract

In this paper, a synchronisation problem is studied for a class of nonlinear leader-following systems. Based on adaptive control with some algebra lemmas, we propose a distributed control scheme. This scheme ensures each follower to asymptotically synchronise with the leader. Compared with the existing works where system input powers are assumed one, the input powers considered in this paper are unknown but larger than one. Based on Graph theory, Lyapunov theory and radial basis function (RBF) networks, we design a distributed adaptive supervisory control method. It is proven that the consensus among the leader and followers are achieved and all the signals including tracking errors asymptotically converge to a small neighbourhood of the origin. Finally, simulation results are displayed to demonstrate the effectiveness of control scheme.

Keywords:

• Intelligent control
• neural networks-based control
• supervisory control
• command filter
• multi-agent systems

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

No data was used for the research described in the article.

Additional information

Funding

This research was supported in part by the National Natural Science Foundation of China [62273300, 61873229].