Dynamic event-triggered consensus for discrete-time hidden Markov multi-agent systems with partially unknown probabilities

Abstract

This paper addresses the consensus problem of Markov jump multi-agent systems under dynamic event-triggered communication. In order to utilize the limited network resources reasonably and improve the efficiency of data transmission, a dynamic event-triggered method is adopted. Considering the challenge of obtaining system mode information, a hidden Markov model is introduced. On this basis, because of the limitation of mode information acquisition, the case with partially unknown probabilities both exist in the transition probability matrix and the observation probability matrix is discussed, which makes the conclusion realistic. Moreover, a sampled-data consensus protocol is proposed, and based on the Lyapunov stability theory, several sufficient conditions are derived to ensure the consensus of the system under specified [Formula: see text] performance. Finally, a numerical example is given to demonstrate the effectiveness of the proposed protocol.