Event-triggered group consensus for one-sided Lipschitz multi-agent systems with input saturation

This paper investigates event-triggered group consensus problem for multi-agent sys-tems with input saturation under fixed topology and Markovian switching topologies respectively. The agent dynamics is described by one-sided Lipschitz nonlinear function, which covers a broad family of nonlinear systems and includes the well-known Lipschitz system as a special case. A novel distributed dynamic event-triggered communication scheme is proposed based on the local stochastic sampling information among the agents, which includes some existing schemes as special cases. A new stochastic sampled-data dependent error system is obtained by model transformation, based on the discontinuous Lyapunov stability theory, some local group consensus criteria in mean square can be derived for fixed topology and Markovian switching topologies, respectively. Moreover, an optimization algorithm is introduced to estimate the region of initial conditions. Finally, two specific numerical examples are employed to illustrate the effectiveness of the theoretical results.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

This paper investigates the event-triggered group consensus problem for multi-agent systems with input saturation under both fixed topology and Markovian switching topologies. The agent dynamics are described by a one-sided Lipschitz nonlinear function, which covers a wide range of nonlinear systems. A novel distributed dynamic event-triggered communication scheme based on local stochastic sampling information is proposed. The paper also derives local group consensus criteria for the mean square condition under fixed topology and Markovian switching topologies using a stochastic sampled-data dependent error system obtained through model transformation. An optimization algorithm is introduced for estimating the region of initial conditions, and numerical examples are provided to illustrate the effectiveness of the theoretical results. (c) 2023 Elsevier B.V. All rights reserved.