Dynamic Event-Triggered Consensus Control for Markovian Switched Multi-Agent Systems: A Hybrid Neuroadaptive Method

Abstract

This paper presents a solution to the consensus problem for a particular category of uncertain switched multi-agent systems (MASs). In these systems, the communication topologies between agents and the system dynamics are governed by a time-homogeneous Markovian chain in a stochastic manner. To address this issue, we propose a novel neuroadaptive distributed dynamic event-triggered control (DETC) strategy. By leveraging stochastic Lyapunov theory and matrix inequality methodology, we establish sufficient conditions for practical ultimate mean square consensus (UMSBC) of MASs using a combination of neural networks (NNs) adaptive control strategy and DETC method. Our approach employs a distributed adaptive NNs DETC mechanism in MASs with unknown nonlinear dynamics and upgrades it at the moment of event sampling in an aperiodic manner, resulting in significant savings in computation and resources. We also exclude the Zeno phenomenon. Finally, we provide numerical examples to demonstrate the feasibility of our proposed approach, which outperforms existing approaches.

Leader-Follower Quasi-Consensus of Multi-Agent Systems with Packet Loss Using Event-Triggered Impulsive Control

“…As a result, Zeno behavior can also be ruled out in Case 3. To sum up, if γθ < ln(λ 1 β 1 ) − ln(λ 2 ), the ETM (6) does not exist Zeno behavior.…”

“…Remark 2. From ETM (6), it has been shown that the designed ETM has two operation modes: triggering at t k−1 + θ or t * k . The first case uses the maximum trigger interval and generates impulses called forced impulses in this paper.…”

“…As time advances, multi-agent systems have become a prominent field within the study of complex systems science [3]. In recent years, complex network theory has received tremendous attention from numerous researchers as a powerful tool for investigating complexity science and complex systems [4][5][6]. Multi-agent systems are typical complex systems.…”

Adaptive Neural Control for a Class of Random Fractional-Order Multi-Agent Systems with Markov Jump Parameters and Full State Constraints

“…Note that the relevant results obtained apply only to the single systems with Markov jump parameters. Particularly, with regard to Markov jump MASs, various control strategies have been proposed to deal with the consensus problem of Markov jump MASs, such as event-triggered control [29,30], output feedback control [31], and robust control [32]. For high-order nonlinear Markov jump MASs, how to achieve target control through the adaptive neural control method needs to be further explored.…”