Dynamic event-triggered mechanism-based output consensus of nonlinear multi-agent systems via improved dynamic surface control approach

Abstract

A triggered communication mechanism-based adaptive control strategy is proposed for output consensus of a class of uncertain nonlinear multi-agent systems. A distributed estimator is constructed via intermittent communication with their own neighbours. This estimator provides desired trajectory for parts of followers who are not able to access to leader's information directly. Recursive sliding-modes and nonlinear gain functions are applied for performance improvement of traditional dynamic surface control approaches. Also, an adaptive parameter is introduced for neural networks' weights, and it is for simplification of computational complexity in our control strategy. In theory, it is proven that all signals in the multi-agent system are ultimately bounded, that consensus tracking errors converge to a neighbourhood around the origin, and that there exists Zeno-free behaviour. Three simulation examples validate the effectiveness of the proposed strategy.

Keywords:

• Dynamic surface adaptive control
• dynamic event-triggered mechanism
• neural networks
• nonlinear gain
• recursive sliding-mode

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 61873130, 61833011 and 61833008], in part by the Natural Science Foundation of Jiangsu Province [grant numbers BK20191377 and BK20191376], in part by 1311 Talent Project of Nanjing University of Posts and Telecommunications, in part by Scientific Foundation of Nanjing University of Posts and Telecommunications (NUPTSF) [grant numbers NY220102, NY220194 and 2020XZZ11], in part by Postgraduate Research & Practice Innovation Program of Jiangsu Province [grant number KYCX20_0821].