LAUSR

This brief studies the event-triggered cooperative tracking problem for multiple autonomous agents described by Lipschitz-type nonlinear dynamics. A new kind of event-triggered cooperative tracking strategy is developed by using model-based local sampled state information and the state-dependent gain approach. Compared with some event-triggered cooperative tracking strategies in existing works, the dynamics of agents is extended to Lipschitz-type nonlinearity in this brief, which can better meet the practical engineering needs. Besides, the cooperative tracking algorithms overcome the infeasibility of linear matrix inequality (LMI) conditions, and the cooperative tracking problem can be addressed by only solving an algebraic Riccati equation (ARE), which is feasible if the system is stabilizable. Finally, a simulation example is presented to verify the theoretical analysis and the effectiveness of the control strategy.