Periodic sampled-data-based dynamic event-triggered consensus of linear multiagent systems via decentralized output feedback stabilization approach

Abstract

This article investigates the double dynamic event-triggered consensus of linear multiagent systems with periodic sampled-data via a novel decentralized output feedback stabilization approach. A novel dynamic event-triggered strategy based on periodic sampled-data is proposed for the sensor-to-controller channel and controller-to-actuator channel, respectively. This strategy can further reduce the number of triggers without affecting the system’s performance. We use a state linear transformation based on a directed spanning tree of the communication topology to equivalently transform the consensus problem into the decentralized output feedback stabilization problem of the corresponding edge-state system and propose a decentralized pole assignment approach to design the gain matrix for the consensus protocol. Then, by employing the Lyapunov function combined with the lossless S-procedure, we derive consensus criteria containing matrix inequalities. Finally, numerical examples are provided to verify the effectiveness of the proposed approach.