Event-Triggered Distributed Average Tracking Control for Lipschitz-Type Nonlinear Multiagent Systems

This article investigates the event-triggered distributed average tracking (ETDAT) control problems for the Lipschitz-type nonlinear multiagent systems with bounded time-varying reference signals. By using the state-dependent gain design approach and event-triggered mechanism, two types of ETDAT algorithms called: 1) static and 2) adaptive-gain ETDAT algorithms are developed. It is the first time to introduce the event-triggered strategy into DAT control algorithms and investigate the ETDAT problem for multiagent systems with Lipschitz nonlinearities, which is more practical in real physical systems and can better meet the needs of practical engineering applications. Besides, the adaptive-gain ETDAT algorithms do not need any global information of the network topology and are fully distributed. Finally, a simulation example of the Watts-Strogatz small-world network is presented to illustrate the effectiveness of the adaptive-gain ETDAT algorithms.

Automated summary

This article investigates the event-triggered distributed average tracking (ETDAT) control problems for Lipschitz-type nonlinear multiagent systems with bounded time-varying reference signals. Two types of ETDAT algorithms, static and adaptive-gain, are developed using the state-dependent gain design approach and event-triggered mechanism. The study introduces the event-triggered strategy into DAT control algorithms for the first time and explores the ETDAT problem for multiagent systems with Lipschitz nonlinearities, which is more practical for real physical systems and meets the needs of practical engineering applications.