An impulsive framework for consensus learning via event-triggered scheme

This paper proposes a novel impulsive framework to study the leader-following consensus in multi-agent systems with an event-triggered mechanism. To be specific, we design an impulsive model to reflect information exchange under event-triggered strategy. Hence the stabilization of this impulsive model can sufficiently guarantee that all followers in original multiagent systems can track the leader finally. The key idea is that impulses happen only when it is needed rather than elapse of time in conventional impulsive schemes. Moreover, we also apply an impulsive model to investigate the case where followers' states with the external disturbance is available instead of their exact states. Thus leader-following consensus can be achieved under well-designed event-triggered protocols.

[1]  Paulo Tabuada,et al.  Event-Triggered Real-Time Scheduling of Stabilizing Control Tasks , 2007, IEEE Transactions on Automatic Control.

[2]  Kairui Chen,et al.  Adaptive leader-following consensus of nonlinear multi-agent systems with jointly connected topology , 2015, The 27th Chinese Control and Decision Conference (2015 CCDC).

[3]  Jinde Cao,et al.  Second-order consensus in multi-agent dynamical systems with sampled position data , 2011, Autom..

[4]  Karl Henrik Johansson,et al.  Distributed Event-Triggered Control for Multi-Agent Systems , 2012, IEEE Transactions on Automatic Control.

[5]  James Lam,et al.  Quasi-synchronization of heterogeneous dynamic networks via distributed impulsive control: Error estimation, optimization and design , 2015, Autom..

[6]  Jun Wang,et al.  Robust Synchronization of Multiple Memristive Neural Networks With Uncertain Parameters via Nonlinear Coupling , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[7]  Shengyuan Xu,et al.  A distributed event-triggered scheme for discrete-time multi-agent consensus with communication delays , 2014 .

[8]  Feng Qian,et al.  Network-based leader-following consensus of nonlinear multi-agent systems via distributed impulsive control , 2017, Inf. Sci..

[9]  Jinde Cao,et al.  A New Framework for Analysis on Stability and Bifurcation in a Class of Neural Networks With Discrete and Distributed Delays , 2015, IEEE Transactions on Cybernetics.

[10]  Jinde Cao,et al.  A unified synchronization criterion for impulsive dynamical networks , 2010, Autom..

[11]  Guanghui Wen,et al.  Consensus of multi‐agent systems with nonlinear dynamics and sampled‐data information: a delayed‐input approach , 2013 .

[12]  Jinde Cao,et al.  Synchronization Control for Nonlinear Stochastic Dynamical Networks: Pinning Impulsive Strategy , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[13]  Qing-Long Han,et al.  A distributed event-triggered transmission strategy for sampled-data consensus of multi-agent systems , 2014, Autom..

[14]  Jinde Cao,et al.  Leader-following consensus of general linear multi-agent systems: Event-triggered schemes , 2015, 2015 10th Asian Control Conference (ASCC).

[15]  Jinde Cao,et al.  Leader-following consensus of non-linear multi-agent systems with jointly connected topology , 2014 .

[16]  Tongwen Chen,et al.  Event based agreement protocols for multi-agent networks , 2013, Autom..

[17]  Jinde Cao,et al.  A new protocol for finite-time consensus of detail-balanced multi-agent networks. , 2012, Chaos.

[18]  W. P. M. H. Heemels,et al.  Output-Based Event-Triggered Control With Guaranteed ${\cal L}_{\infty}$-Gain and Improved and Decentralized Event-Triggering , 2012, IEEE Transactions on Automatic Control.